Digital signal processors (DSPs) with very long instruction word (VLIW) data-path architectures are increasingly being deployed on embedded devices for multimedia processing applications. To reduce the power consumption and design cost of VLIW DSP processors, distributed register files and multibank register architectures are being adopted to reduce the number of read and write ports associated with register files, which presents new challenges for devising compiler optimization schemes. This paper addresses the issues of reducing the spill code for a VLIW DSP with distributed register files. Spill code produced by register allocation is traditionally handled by memory spills, but the multibank register-file architecture provides the opportunity to spill-out register values onto different register banks. We present a conceptual framework based on the universal and the proxy interference graphs to model the live ranges of registers for spilling codes to different register banks. Heuristic algorithms are then developed on the basis of this concept. By heuristically estimating the register pressure for each register file, we treat different register banks as optional spilling locations in addition to traditional spilling to memory. Experiments were performed on the parallel architecture core VLIW DSP with distributed register files by incorporating our proposed optimization schemes into an Open64-based compiler. The experimental results show that our approach can improve the performances on average for DSPStone and MiBench benchmarks with spilling cases by 7.1% and 21.6%, respectively, compared with the one always handling spill code in memory. Copyright © 2013 John Wiley & Sons, Ltd.

Common reverse nearest neighbor queries in spatial database run in an inefficient way because they need to check a query result with almost every nearest neighbor. This wastes many time and resources, making this approach unsuitable for mobile computation. Instead of using the neighbors as candidates for the query result, a region approach can be used to answer the query. By using this approach, any objects located in the region will be considered candidate results for the query. To reduce the cost of creating the region, we introduce the concept of a contact zone, a method that can identify the right region generator points without having to process the whole points in the space, hence make reverse nearest neighbor queries by region possible to be run in mobile devices.Copyright © 2013 John Wiley & Sons, Ltd.

In ubiquitous computing environments, applications must be able to respond to dynamic context changes in order to provide suitable services to users. A promising solution consists of developing context aware applications. In this paper, we present an approach that supports context awareness in a ubiquitous computing environment. The proposed approach focuses on handling context information including context monitoring/acquisition, context interpretation, context classification, context storage, context analysis, and context aware adaptation. In this paper, we focus on context classification and context analysis. We present a context classification approach, which takes into account the context parameter evolution behavior. Then, we define a context analysis approach for ubiquitous computing environments. The proposed analysis approach aims at analyzing contextual information and detecting significant changes. The proposed analysis approach uses a threshold comparison technique in order to detect context changes. When relevant context changes are detected, the context aware application will be notified to trigger its suitable process dynamically in order to deal with the changes. Copyright © 2013 John Wiley & Sons, Ltd.

This paper presents two routing games, a unicast and a multicast routing game, for wireless sensor networks. We analyze the actions of nodes inside/outside lowest cost path (LCP) and draw their payoff functions. Our simulation shows that this evolutionary game theory scheme has several advantages over a widely used collusion–resistant routing scheme. All nodes, either out of LCP or in LCP, will ultimately choose the strategy ‘transmit’. To improve the payoff, nodes in LCP should either minimize their actual forwarding cost or maximize their claimed cost as long as it remains in the LCP, whereas minimizing the actual claimed cost is not an optimal option for nodes out of LCP. Copyright © 2013 John Wiley & Sons, Ltd.

In 2009, Wu and Lin introduced the concept of self-certified proxy convertible authenticated encryption (SP-CAE) by integrating self-certified public-key system and designated verifier proxy signature with message recovery. They also presented the first SP-CAE scheme which is based the discrete logarithm problem. However, Wu-Lin scheme is not secure as Xie et al. recently showed that this scheme is existentially forgeable under adaptive chosen warrants, unconfidentiable and verifiable under adaptive chosen messages and designated verifiers. In this paper, we first discuss the security requirements of SP-CAE and then formally define unforgeability, message confidentiality, and unverifiability. Consequently, the first complete formal model of SP-CAE is proposed. After that, we propose a provably secure SP-CAE scheme by using two-party Schnorr signature introduced by Nicolosi et al. in 2003. Finally, we prove the formal security of the proposed scheme in the random oracle model under the discrete logarithm assumption.Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, a new local region-based active contour model that is driven by normalized local image fitting energy is proposed. By considering the local image fitting energy to extract the local image information, our model can effectively and efficiently segment images with intensity inhomogeneities. In addition, to keep the smoothness of the level set function, the time-consuming reinitialization step widely adopted in traditional level set methods can be avoided by introducing a Gaussian filtering process. Experimental results on synthetic and real images demonstrate that the proposed model has much less CPU time and sensitivity to the initial contour than the well-known local binary fitting model. Copyright © 2013 John Wiley & Sons, Ltd.

With the incessant expansion of applications and the frequent update of the software, Science Gateway for Massive Remote Sensing Image Processing (SGMRSIP), developed by client/server model or traditional browser/server model, has received more and more challenges. Fortunately, the Web 2.0 technologies, proposed in recent years, bring us a new user experience (UE) that has a fast response speed and a good interface. In particular, the remote sensing image can be processed smoothly in the absence of client software by Web 2.0 technologies. Hence, a Web 2.0-based browser/server model is designed for SGMRSIP to enhance the UE in this paper. Firstly, functions of a parallel remote sensing image processing portal, based on high performance cluster and client/server model, are summarized. And then, a Web 2.0-based interaction model is built, and all these functions are accomplished again on the basis of this model. Finally, the Web 2.0-based Science Gateway is achieved. In addition, we design different workflows for different satellite data, and all the processing tasks are finished successfully to verify the feasibility of this Science Gateway. The experimental results showed that the software scalability and interaction were improved and a better UE was achieved, compared with the existing SGMRSIP. Copyright © 2013 John Wiley & Sons, Ltd.

To restrain pseudo-Gibbs phenomenon, low contrast and blurred phenomenon in the process of image enhancement, a new method based on the nonsubsampled contourlet transform and the unsharp masking is proposed in this paper. The proposed method utilizes the shift-invariance of nonsubsampled contourlet transform to restrain the pseudo-Gibbs phenomenon, and then enhance details of the image by unsharp masking. We achieved an increase in image definition by 54.5%, the mean increased by 15.6%, whereas the standard deviation increased by 54.5% compared with the unsharp masking method. To the noisy image, we achieved an increase in image definition by 35.4%, the mean increased by 2.2%, whereas the standard deviation increased by 34.9% compared with the unsharp masking method. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, a new class of generalized cyclotomic binary sequences with period of 2pq is established. The linear complexity and minimal polynomials of proposed sequences of length 2pq are determined. The results show that such sequences of length 2pq have high linear complexity. Copyright © 2013 John Wiley & Sons, Ltd.

Self-caring IT systems are those that can proactively avoid system failures rather than reactively handle failures after they have occurred. In this paper, we focus on failures in which a MapReduce job is unable to execute within an service-level agreement based completion time. The existing fault-tolerance capability provided by MapReduce frameworks such as Hadoop, is simple and the penalty associated with handling faults could potentially lead to excessive job execution times. Our goal in this paper is to bring out the severity of this penalty for different job and framework parameters. We quantitatively evaluate the penalty in execution time associated with node faults using the MRPerf simulator. We then perform an empirical study of penalties on a virtualized testbed consisting of Xen domains, by varying system characteristics along four dimensions: hardware, application, dataset, and fault types. Through simulation and empirical results, we show that job-completion-time service-level agreement violations can be reduced using dynamic resource scaling. Scaling leverages, the elastic properties of a virtualized environment, to mitigate execution time penalties and hence proactively avoids a potential job failure. We show that using resource scaling, performance penalties can be decreased to less than 5% of the no-fault execution time, at minimal additional cost. Copyright © 2013 John Wiley & Sons, Ltd.

MapReduce programming paradigm has been widely applied to solve large-scale data-intensive problems. Intensive studies of MapReduce scheduling have been carried out to improve MapReduce system performance. Delay scheduling is a common way to achieve high data locality and system performance. However, inappropriate delays can lead to low system throughput and potentially break the original job priority constraints. This paper proposes a deadline-enabled delay (DLD) scheduling algorithm that optimizes job delay decisions according to real-time resource availability and resource competition, while still meets job deadline constraints. Experimental results illustrate that the resource availability estimation method of DLD is accurate (92%). Compared with other approaches, DLD reduces job turnaround time by 22% in average while keeping a high locality rate (88%).Copyright © 2013 John Wiley & Sons, Ltd.

The last few years have seen an increase in the use of e-services as a natural consequence of the service-based economy growth. However, the distributed nature of services can raise several heterogeneity problems that can hamper the widespread adoption of service-oriented architectures (SOA). In this paper, we are interested in data heterogeneity problems that can occur when using composite services. We propose a service-based approach for automatically inserting appropriate mediation services in service compositions to resolve structural heterogeneities in their data flow. This mediation is ensured through a special kind of Web services called data mapping Web services. In addition, in order to be in agreement with the distributed nature of the Web, we propose a decentralized solution to publish/discover these services. We demonstrate how our approach applies to the data structural heterogeneity problem in data providing service compositions as a particular data heterogeneity case. Provided experimental results show that our approach is efficient in realistic situations. Copyright © 2013 John Wiley & Sons, Ltd.

The fast multipole method (FMM) is a complex, multi-stage algorithm over a distributed tree data structure, with multiple levels of parallelism and inherent data locality. X10 is a modern partitioned global address space language with support for asynchronous activities. The parallel tasks comprising FMM may be expressed in X10 by using a scalable pattern of activities. This paper demonstrates the use of X10 to implement FMM for simulation of electrostatic interactions between ions in a cyclotron resonance mass spectrometer. X10's task-parallel model is used to express parallelism by using a pattern of activities mapping directly onto the tree. X10's work stealing runtime handles load balancing fine-grained parallel activities, avoiding the need for explicit work sharing. The use of global references and active messages to create and synchronize parallel activities over a distributed tree structure is also demonstrated. In contrast to previous simulations of ion trajectories in cyclotron resonance mass spectrometers, our code enables both simulation of realistic particle numbers and guaranteed error bounds. Single-node performance is comparable with the fastest published FMM implementations, and critical expansion operators are faster for high accuracy calculations. A comparison of parallel and sequential codes shows the overhead of activity management and work stealing in this application is low. Scalability is evaluated for 8k cores on a Blue Gene/Q system and 512 cores on a Nehalem/InfiniBand cluster. Copyright © 2013 John Wiley & Sons, Ltd.

This article is dedicated to techniques and theories of image fusion in automatic ways and addresses two issues—the parameter setting and quality assessment. Optimal parameters are in demand for specific applications or comparison between fusion methods because, as basic evidence, different parameters bring different fusion effects varying over a large range. In this paper, we propose a general framework of online parameter training to search optimal values that best suit input images. Furthermore, we optimized the compute-intensive training process using parallelization and genetic algorithm, as well as patches extraction. We also propose a metric—spatial and spectral distortion—as the learning target. The spatial and spectral distortion is a fuzzy combination of mean potential energy measuring spatial distortion and Q4 measuring spectral distortion. Optimization validation on weighted Gram–Schmidt fusion indicated linear or superlinear acceleration ability, which proved that the proposed learning framework can speed up the learning process of image fusion to an acceptable time, and can thus be applied to high-performance platforms to process large volumes of data. Copyright © 2013 John Wiley & Sons, Ltd.

Spherical harmonic transforms (SHT) are at the heart of many scientific and practical applications ranging from climate modelling to cosmological observations. In many of these areas, new cutting-edge science goals have been recently proposed requiring simulations and analyses of experimental or observational data at very high resolutions and of unprecedented volumes. Both these aspects pose formidable challenge for the currently existing implementations of the transforms. This paper describes parallel algorithms for computing SHT with two variants of intra-node parallelism appropriate for novel supercomputer architectures, multi-core processors and Graphic Processing Units (GPU). It also discusses their performance, alone and embedded within a top-level, Message Passing Interface-based parallelisation layer ported from the S²HAT library, in terms of their accuracy, overall efficiency and scalability. We show that our inverse SHT run on GeForce 400 Series GPUs equipped with latest Compute Unified Device Architecture architecture (Fermi) outperforms the state of the art implementation for a multi-core processor executed on a current Intel Core i7-2600K. Furthermore, we show that an Message Passing Interface/Compute Unified Device Architecture version of the inverse transform run on a cluster of 128 Nvidia Tesla S1070 is as much as 3 times faster than the hybrid Message Passing Interface/OpenMP version executed on the same number of quad-core processors Intel Nehalem for problem sizes motivated by our target applications. Performance of the direct transforms is however found to be at the best comparable in these cases. We discuss in detail the algorithmic solutions devised for the major steps involved in the transforms calculation, emphasising those with a major impact on their overall performance and elucidates the sources of the dichotomy between the direct and the inverse operations.Copyright © 2013 John Wiley & Sons, Ltd.

Gadget is a simulation application for N-body and smoothed particle hydrodynamics problems in cosmology, and it is widely applied in solving series of cosmological problems. N-body focuses on the motion of the interaction of N particles, and smoothed particle hydrodynamics is a fluid simulation algorithm that studies the movement of fluid through particle simulation. Most scholars focus their attention on accelerating Gadget on multi-core CPU or graphics processing units (GPUs) platforms. However, these research activities failed to achieve CPU–GPU hybrid computing, which resulted in tremendous waste of CPU computing resources.

In this paper, we propose a CPU–GPU hybrid parallel strategy to accelerate Gadget-2, a massively parallel structure formation code for cosmological simulations. This strategy uses CPU and GPU to process the calculation of short-range force. To ensure CPU and GPU workload balance, a dynamic task allocation scheme is proposed according to the computational performance difference between the CPU and GPU.

Experimental results showed that our CPU–GPU hybrid parallel strategy achieved an overall speedup factor of 18.6 and a partial speedup factor for short-range force calculation of 28.35 compared with a single-core CPU implementation for particles in million-size magnitudes. Moreover, compared with a GPU platform that contained 12 CPU cores and one GPU, our hybrid parallel strategy obtained overall speedup and partial speedup factors of 6% and 20%, respectively. Furthermore, the scalability of the hybrid strategy is very fine – its performance will be enhanced when the problem scale is increasing. However, this strategy also has its limitation that the performance enhancement will be decreasing if the ratio(the number of CPU cores divides that of the GPU cards) reduces. Finally, in our hybrid strategy, the CPU coefficient of utilization improved by 17.14% or better. Copyright © 2013 John Wiley & Sons, Ltd.

Cloud computing environments are now widely available and are being increasingly utilized for technical computing. They are also being touted for high-performance computing (HPC) applications in science and engineering. For example, Amazon Elastic Compute Cloud (EC2) Services offers specialized Cluster Compute instance types to run HPC applications. In this paper, we compare the performance characteristics of two Amazon EC2 HPC instance types with that of National Aeronautics and Space Administration's (NASA) Pleiades supercomputer, an SGI® ICE™ cluster. For this study, we utilized the HPC Challenge kernels and the NAS Parallel Benchmarks along with four full-scale applications from the repertoire of codes that are being used by NASA scientists and engineers. We compare the total runtime of these codes for varying number of cores. We also break out the computation and communication times for a subset of these applications to explore the effect of interconnect differences on the two systems. In general, the single node performance of the two platforms is equivalent. However, for most of the codes when scaling to larger core counts, the performance of the EC2 HPC instances generally lags that of Pleiades because of worse network performance of the former. In addition to analyzing application performance, we also briefly touch upon the overhead due to virtualization and the usability of cloud environments such as Amazon EC2. Published 2013. This article is a U.S. Government work and is in the public domain in the U.S.A.

Since the beginning of the multicore era, parallel processing has become prevalent across the board. On a traditional multicore system, a single operating system manages all cores and schedules threads and processes among them, inherently supported by hardware-implemented cache coherence protocols. However, a further growth of the number of cores per system implies an increasing chip complexity, especially with respect to the cache coherence protocols. Therefore, a very attractive alternative for future many-core systems is to waive the hardware-based cache coherency and to introduce a software-oriented message-passing based architecture instead: a so-called Cluster-on-Chip architecture. Intel's Single-chip Cloud Computer (SCC), a many-core research processor with 48 non-coherent memory-coupled cores, is a very recent example for such a cluster-on-chip architecture. The SCC can be configured to run one operating system instance per core by partitioning the shared main memory in a strict manner. However, it is also possible to access the shared main memory in an unsplit and concurrent manner, provided that either the caches are disabled or the cache coherency is then ensured by software. In this article, we detail our experiences gained while developing low-level software for message-passing and shared-memory programming on the SCC. We present an SCC-customized MPI library (called SCC-MPICH) as well as a shared virtual memory system (called MetalSVM) for the SCC. In doing so, we evaluate the potential of both programming models and we show how these models can be improved especially with respect to the SCC's many-core architecture. Copyright © 2013 John Wiley & Sons, Ltd.

The emergence of cloud computing has brought the opportunity to use large-scale compute infrastructures for a broader and broader spectrum of applications and users. As the cloud paradigm gets attractive for the ‘elasticity’ in resource usage and associated costs (the users only pay for resources actually used), cloud applications still suffer from the high latencies and low performance of cloud storage services. As Big Data analysis on clouds becomes more and more relevant in many application areas, enabling high-throughput massive data processing on cloud data becomes a critical issue, as it impacts the overall application performance. In this paper, we address this challenge at the level of cloud storage. We introduce a concurrency-optimized data storage system (called TomusBlobs), which federates the virtual disks associated to the Virtual Machines running the application code on the cloud. We demonstrate the performance benefits of our solution for efficient data-intensive processing by building an optimized prototype MapReduce framework for Microsoft's Azure cloud platform on the basis of TomusBlobs. Finally, we specifically address the limitations of state-of-the-art MapReduce frameworks for reduce-intensive workloads, by proposing MapIterativeReduce as an extension of the MapReduce model. We validate the aforementioned contributions through large-scale experiments with synthetic benchmarks and with real-world applications on the Azure commercial cloud by using resources distributed across multiple data centers; they demonstrate that our solutions bring substantial benefits to data-intensive applications compared with approaches relying on state-of-the-art cloud object storage. Copyright © 2013 John Wiley & Sons, Ltd.

Term frequency–inverse document frequency (TF–IDF), one of the most popular feature (also called term or word) weighting methods used to describe documents in the vector space model and the applications related to text mining and information retrieval, can effectively reflect the importance of the term in the collection of documents, in which all documents play the same roles. But, TF–IDF does not take into account the difference of term IDF weighting if the documents play different roles in the collection of documents, such as positive and negative training set in text classification. In view of the aforementioned text, this paper presents a novel TF–IDF-improved feature weighting approach, which reflects the importance of the term in the positive and the negative training examples, respectively. We also build a weighted voting classifier by iteratively applying the support vector machine algorithm and implement one-class support vector machine and Positive Example Based Learning methods used for comparison. During classifying, an improved 1-DNF algorithm, called 1-DNFC, is also adopted, aiming at identifying more reliable negative documents from the unlabeled examples set. The experimental results show that the performance of term frequency inverse positive–negative document frequency-based classifier outperforms that of TF–IDF-based one, and the performance of weighted voting classifier also exceeds that of one-class support vector machine-based classifier and Positive Example Based Learning-based classifier. Copyright © 2013 John Wiley & Sons, Ltd.

Large-scale scientific experiments based on computer simulations are typically modeled as scientific workflows, which eases the chaining of different programs. These scientific workflows are defined, executed, and monitored by scientific workflow management systems (SWfMS). As these experiments manage large amounts of data, it becomes critical to execute them in high-performance computing environments, such as clusters, grids, and clouds. However, few SWfMS provide parallel support. The ones that do so are usually labor-intensive for workflow developers and have limited primitives to optimize workflow execution. To address these issues, we developed workflow algebra to specify and enable the optimization of parallel execution of scientific workflows. In this paper, we show how the workflow algebra is efficiently implemented in Chiron, an algebraic based parallel scientific workflow engine. Chiron has a unique native distributed provenance mechanism that enables runtime queries in a relational database. We developed two studies to evaluate the performance of our algebraic approach implemented in Chiron; the first study compares Chiron with different approaches, whereas the second one evaluates the scalability of Chiron. By analyzing the results, we conclude that Chiron is efficient in executing scientific workflows, with the benefits of declarative specification and runtime provenance support. Copyright © 2013 John Wiley & Sons, Ltd.

E2, a block cipher, is an Advanced Encryption Standard candidate designed and submitted by Nippon Telegraph and Telephone Corporation. It employs a Feistel structure as global structure and two-layer substitution–permutation network structure in round function. The conservative structure makes E2 immune to kinds of current cryptanalysis. Previously, there is no result of impossible differential attacks on E2 because it was once supposed to have no more than five-round impossible differential characteristic. In this paper, we present a series of six-round impossible differential characteristics of E2 with/without initial transformation (IT)/ final transformation (FT) functions. Based on these impossible differentials, the immunity of E2 against impossible differential cryptanalysis is evaluated. We perform a seven-round attack on tweaked E2 (E2 without IT and FT ) with 128, 192, and 256 bits key and an eight-round attack on tweaked E2 with 256 bits key. The seven-round attack requires about 2¹²⁰ chosen plaintexts and 2^115.5 seven-round encryptions; the eight-round attack needs 2¹²¹ chosen plaintexts and less than 2²¹⁴ eight-round encryptions. We also discuss the seven-round attack on E2 with IT or FT, and the result shows that the attack has the same complexities with the seven-round attack on tweaked E2. Copyright © 2013 John Wiley & Sons, Ltd.

This paper addresses how the benefits of cloud-based infrastructure can be harnessed for analytical workloads. Often, the software handling analytical workloads is not developed by a professional programmer but on an ad hoc basis by analysts in high-level programming environments such as R or MATLAB. The goal of this research is to allow Analysts to take an analytical job that executes on their personal workstations and with minimum effort execute it on cloud infrastructure and manage both the resources and the data required by the job. If this can be facilitated gracefully, then the Analyst benefits from on-demand resources, low maintenance cost and scalability of computing resources, all of which are offered by the cloud. In this paper, a Platform for Parallel R-based Analytics on the Cloud (P2RAC) that is placed between an Analyst and a cloud infrastructure is proposed and implemented. P2RAC offers a set of command-line tools for managing the resources, such as instances and clusters, the data and the execution of the software on the Amazon Elastic Computing Cloud infrastructure. Experimental studies are pursued using two parallel problems and the results obtained confirm the feasibility of employing P2RAC for solving large-scale analytical problems on the cloud.Copyright © 2013 John Wiley & Sons, Ltd.

Visual and interactive data exploration requires fast and reliable tools for embedding of an original data space in 3(2)-dimensional Euclidean space. Multidimensional scaling (MDS) is a good candidate. However, owing to at least O(M²) memory and time complexity, MDS is computationally demanding for interactive visualization of data sets consisting of order of 10⁴ objects on computer systems, ranging from PC with multicore CPU processor, graphics processing unit (GPU) board to midrange MPI clusters. To explore interactively data sets of that size, we have developed novel efficient parallel algorithms for MDS mapping based on virtual particle dynamics. We demonstrate that the performance of our MDS algorithms implemented in compute unified device architecture environment on a PC equipped with a modern GPU board (Tesla M2090, GeForce GTX 480) is considerably faster than its MPI/OpenMP parallel implementation on the modern midrange professional cluster (10 nodes, each equipped with 2x Intel Xeon X5670 CPUs). We also show that the hybridized two-level MPI/CUDA implementation, run on a cluster of GPU nodes, can additionally provide a linear speedup. Copyright 2013 John Wiley & Sons, Ltd.

One of the foundations of science is that researchers must publish the methodology used to achieve their results so that others can attempt to reproduce them. This has the added benefit of allowing methods to be adopted and adapted for other purposes. In the field of e-Science, services – often choreographed through workflow, process data to generate results. The reproduction of results is often not straightforward as the computational objects may not be made available or may have been updated since the results were generated. For example, services are often updated to fix bugs or improve algorithms. This paper addresses these problems in three ways. Firstly, it introduces a new framework to clarify the range of meanings of ‘reproducibility’. Secondly, it describes a new algorithm, PDIFF, that uses a comparison of workflow provenance traces to determine whether an experiment has been reproduced; the main innovation is that if this is not the case then the specific point(s) of divergence are identified through graph analysis, assisting any researcher wishing to understand those differences. One key feature is support for user-defined, semantic data comparison operators. Finally, the paper describes an implementation of PDIFF that leverages the power of the e-Science Central platform that enacts workflows in the cloud. As well as automatically generating a provenance trace for consumption by PDIFF, the platform supports the storage and reuse of old versions of workflows, data and services; the paper shows how this can be powerfully exploited to achieve reproduction and reuse. Copyright © 2013 John Wiley & Sons, Ltd.

Various template protection techniques have been developed in the past few years, among which cancelable biometrics is a very popular and efficient one. It uses a noninvertible transformation to map an original template to a transformed domain to prevent the recovery of the original template from a compromised transformed template. Generally, cross-template attacks over cancellable template schemes are evaluated through statistic independence metrics. In this paper, we investigate approaches to launch attacks through cryptanalysis. Four typical cancelable fingerprint template design algorithms have been investigated by applying the attack via record multiplicity attack to retrieve the original template. Concrete attack examples are also given to make the demonstration more intuitive and comprehensive. The results show that all of them are vulnerable if an attacker can obtain multiple transformed templates and their corresponding transformation parameters. Copyright © 2013 John Wiley & Sons, Ltd.

The main responsibilities of a web server are to listen from the communication channel and to prepare replies to requests. Additional responsibilities include adapting processing activities, for example, through scheduling or request filtering, so as to satisfy Quality of Service (QoS) requirements. Typical QoS-related concerns address behavioural constraints (e.g. response time bounds, satisfiable by scheduling the most urgent requests first) and resource monitoring, for optimal use. Although such concerns are spread across several web server components, they should be handled separately from communication-related ones, for the sake of modularity.

For this purpose, we advocate recourse to aspect-oriented programming and illustrate it by showing how a QoS-related layer can be smoothly superimposed on top of a well-known, unmodified, web server Jigsaw. As part of the provided support, requests are assessed and partitioned into those that can be appropriately handled by using local resources and those needing further resources. For the latter requests, cloud-based resources are gathered. Aspect-orientation enables new QoS-related code to be separated from web server modules so as to keep existing code unaltered, while runtime behaviour is modified with the measures needed to handle QoS concerns. Copyright © 2013 John Wiley & Sons, Ltd.

In the paper, an field-programmable gate array (FPGA)-based framework is described to efficiently accelerate unstructured finite volume computations where the same mathematical expression has to be evaluated at every point of the mesh. The irregular memory access patterns caused by the unstructured spatial discretization are eliminated by a novel mesh node reordering technique, and a special architecture is designed to fully utilize the benefits of the predictable memory access patterns. In the proposed architecture, a fixed-size moving window of the input stream of the reordered state variables is cached into the on-chip memory and a pipelined chain of processing elements, which gets input only from the fast on-chip memory, is used to carry out the computations. The arithmetic unit (AU) of the processing elements is generated from the data flow graph extracted from the given mathematical expression. The data flow graph is partitioned with a novel graph partitioning algorithm to break up the AU into smaller locally controlled parts, which can be more efficiently implemented in FPGA than the globally controlled AU. The proposed architecture and algorithms are presented via a case study solving the Euler equations on an unstructured mesh. On the currently available largest FPGA, the generated architecture contains three processing elements working in a pipelined fashion to provide one order of magnitude speedup compared with a high performance microprocessor and three times speedup compared with a high performance graphics processing unit. Copyright © 2013 John Wiley & Sons, Ltd.

Extract, transform, and load (ETL) processes organized as workflows play an important role in the future data integration for cloud services. ETL designers/administrators need testing data set that is aware of semantics of ETL workflow workloads to evaluate their developed ETL systems. Populating testing ETL systems with meaningful workload data is a difficult task. In this paper, we propose a semantic-aware data generator for ETL workflows. With given ETL workflow models and workload characterizations, the generator is able to generate synthetic data that capture the semantics of ETL activities. This is carried out by a three-staged approach. First, we derive expected cardinalities of all the source, intermediate, and target data sets involved in the ETL workflow model with some user-specified cardinality requirements. Then, with the concept of symbolic test, symbolic data instead of concrete data involved in ETL activities are generated, and semantics of the ETL workflow models are transformed to various constraints over these symbols. At last, concrete data are derived on the basis of resolving constraints. Our generator may facilitate ETL workload test case generation for ETL toolkit performance and function evaluations as well as ETL workflow solution benchmarking. Copyright © 2013 John Wiley & Sons, Ltd.

Chosen-ciphertext security has been well-accepted as a standard security notion for public-key encryption. But in a multi-user surrounding, it may not be sufficient, because the adversary may corrupt some users to obtain the random coins as well as the plaintexts used to generate ciphertexts. The attack is named ‘selective opening attack’. We study how to achieve full-fledged chosen-ciphertext security in selective opening setting directly from the Decisional Diffie–Hellman assumption. Our construction is actually a tag-based public-key encryption scheme free of chameleon hashing and has a tight security reduction to the Decisional Diffie–Hellman assumption and the collision-resistant assumption of hash functions. The tag for each ciphertext is generated in a flexible way to serve the chosen-ciphertext security proof in selective opening settings. Copyright © 2013 John Wiley & Sons, Ltd.

Complex coupled multiphysics simulations are playing increasingly important roles in scientific and engineering applications such as fusion, combustion, and climate modeling. At the same time, extreme scales, increased levels of concurrency, and the advent of multicores are making programming of high-end parallel computing systems on which these simulations run challenging. Although partitioned global address space (PGAS) languages attempt to address the problem by providing a shared memory abstraction for parallel processes within a single program, the PGAS model does not easily support data coupling across multiple heterogeneous programs, which is necessary for coupled multiphysics simulations. This paper explores how multiphysics-coupled simulations can be supported by the PGAS programming model. Specifically, in this paper, we present the design and implementation of the XpressSpace programming system, which extends existing PGAS data sharing and data access models with a semantically specialized shared data space abstraction to enable data coupling across multiple independent PGAS executables. XpressSpace supports a global-view style programming interface that is consistent with the PGAS memory model, and provides an efficient runtime system that can dynamically capture the data decomposition of global-view data-structures such as arrays, and enable fast exchange of these distributed data-structures between coupled applications. In this paper, we also evaluate the performance and scalability of a prototype implementation of XpressSpace by using different coupling patterns extracted from real world multiphysics simulation scenarios, on the Jaguar Cray XT5 system at Oak Ridge National Laboratory.Copyright © 2013 John Wiley & Sons, Ltd.

The Extensible Authentication Protocol (EAP) framework aims to realize a flexible authentication for wireless networks. However, a full EAP authentication needs several round trips between a mobile node and the EAP server, and hence is unacceptable in a process of handoff authentication because of inefficient performance. Considering the advantage of the identity-based cryptography, it is attractive to realize handoff authentication efficiently in the identity-based setting. In this work, we propose a new identity-based handoff authentication scheme in which a special double-trapdoor chameleon hash function is used. Compared with the existing identity-based handoff authentication construction, the main advantage of the proposed scheme eliminates the assumption that the private key generator is fully trusted. Besides, the detailed security analysis shows that the proposed scheme not only satisfies robust security properties but also enjoys desirable efficiency for the real-world applications. Copyright © 2013 John Wiley & Sons, Ltd.

Service selection in service-oriented computing has emerged to be an increasingly important research area. From the client's point of view, in addition to the QoS of a service or a service composition, the trust level becomes an important part. As the complexity of invocation in service composition has been greatly increased, a comprehensive mechanism, which could evaluate both the subjective aspect as trust expression and the objective aspect as QoS, is needed. In this paper, we provide a formal service composition architecture for service selection. In addition, we propose a trust evaluation method for the service composition plan based on the subjective probability theory, based on them, our trust-oriented genetic algorithm (TOGA) is proposed to find a near-optimal service composition plan with QoS constraints. Experimental results have illustrated that our proposed approach can discover the near-optimal solution efficiently. Copyright © 2013 John Wiley & Sons, Ltd.

Certificate-based signature computation is often performed on insecure devices where the signature key is easy to be exposed. To reduce the influence of key exposure, we introduce key-insulated mechanism into certificate-based cryptography and formalize the notion and security model of the certificate-based key-insulated signature scheme. We then present a certificate-based key-insulated signature scheme, which is proven to be existentially unforgeable against adaptive chosen message attacks in the random oracle model. The proposed scheme has potential applications in trusted computing. Copyright © 2013 John Wiley & Sons, Ltd.

Heterogeneous performance prediction models are valuable tools to accurately predict application runtime, allowing for efficient design space exploration and application mapping. The existing performance models require intricate system architecture knowledge, making the modeling task difficult. In this research, we propose a regression-based performance prediction framework for general purpose graphical processing unit (GPGPU) clusters that statistically abstracts the system architecture characteristics, enabling performance prediction without detailed system architecture knowledge. The regression-based framework targets deterministic synchronous iterative algorithms using our synchronous iterative GPGPU execution model and is broken into two components: the computation component that models the GPGPU device and host computations and the communication component that models the network-level communications. The computation component regression models use algorithm characteristics such as the number of floating-point operations and total bytes as predictor variables and are trained using several small, instrumented executions of synchronous iterative algorithms that include a range of floating-point operations-to-byte requirements. The regression models for network-level communications are developed using micro-benchmarks and employ data transfer size and processor count as predictor variables. Our performance prediction framework achieves prediction accuracy over 90% compared with the actual implementations for several tested GPGPU cluster configurations. The end goal of this research is to offer the scientific computing community, an accurate and easy-to-use performance prediction framework that empowers users to optimally utilize the heterogeneous resources.Copyright © 2013 John Wiley & Sons, Ltd.

It is difficult to express the parallelism present in complex computations by using existing higher level abstractions such as MapReduce and Dryad. These computations include applications from wide variety of domains, like Artificial Intelligence, Decision Tree Algorithms, Association Rule Mining, Recommender Systems, Graph Algorithms, Clustering Algorithms, Compute Intensive Scientific Workflows, Optimization Algorithms, and so forth. Their execution graphs introduce new challenges in terms of programmer expressibility and runtime performance such as iterative and recursive computations, shared communication model, and so forth. We propose an extension to MapReduce, called Generate-Map-Reduce (GMR), targeted towards modeling these applications. GMR introduces a new Generate abstraction into the MapReduce framework that captures recursive computations. The runtime also supports iterative jobs and a distributed communication model by using shared data structures. We illustrate recursive computations with GMR by modeling complex applications such as simulated annealing, A* search, and adaptive quadrature computation that require recursive spawning of new tasks to handle variable degree of parallelism. GMR runtime supports caching of common data across iterations in memory and local disks. We illustrate how this caching helps in achieving significant speedup for iterative computations by modeling k-means clustering. Copyright © 2013 John Wiley & Sons, Ltd.

Nowadays, parallel architectures are changing so fast that there is a need for scalable and efficient tools to analyze and predict the performance of parallel applications. Analytical models are proved to be a useful approximation for characterizing parallel algorithms, but developing accurate analytical models is a hard issue, and, in general, they provide coarse performance predictions due to their intrinsic lack of accuracy. In this paper, we describe in detail the Tools for Instrumentation and Analysis (TIA) framework, an easy-to-use tool that automatically obtains accurate performance models by means of analytical expressions. This framework automatizes most of its internal tasks, reducing opportunities for human error, and it only requires the user to focus on the metrics and execution parameters that might influence the performance, those that should be considered in the modeling process. Its main advantage over other tools is that TIA uses model selection techniques that allow the automation of the modeling process. As a case of study, the use of TIA to obtain analytical models of different implementations of the broadcast collective communication in a cluster of multicores is shown. The results obtained by TIA are evaluated and compared with theoretical approaches based on the LogGP model. Copyright © 2013 John Wiley & Sons, Ltd.

This paper proposes a parallel scheme for accelerating parameter sweep applications on a graphics processing unit . By using hundreds of cores on the graphics processing unit, we found that our scheme simultaneously processes multiple parameters rather than a single parameter. The simultaneous sweeps exploit the similarity of computing behaviors shared by different parameters, thus allowing memory accesses to be coalesced into a single access if similar irregularities appear among the parameters’ computational tasks. In addition, our scheme reduces the amount of off-chip memory access by unifying the data that are commonly referenced by multiple parameters and by placing the unified data in the fast on-chip memory. In several experiments, we applied our scheme to practical applications and found that our scheme can perform up to 8.5 times faster than a naive scheme that processes a single parameter at a time. We also include a discussion on application characteristics that are required for our scheme to outperform the naive scheme. Copyright © 2013 John Wiley & Sons, Ltd.

This paper describes the architecture, prototype implementation and performance analysis of a complex event processing engine that can scale up to very large numbers of concurrent events while keeping the requirements on system resources predictable and low. The main innovation of this approach is that each instantiated event pattern is handled by a dedicated Erlang process, instead of a single or shared operating system thread. This in turn, reduces the latency in processing the event processing as it avoids the overheads associated with resource contention. We demonstrate how this approach can achieve linear event processing times under high event loads, using modest computing resources. Copyright © 2013 John Wiley & Sons, Ltd.

Parallel semi-implicit method for pressure-linked equations(SIMPLE) algorithm is used to solve the 3-D incompressible pipe flow problem. In this paper, we proposed a novel parallel SIMPLE algorithm that uses the alternate tiling technique. Firstly, a parallel SIMPLE algorithm based on domain decomposition method was established, and the implementation of domain partition and data exchange was presented. Then, we presented serial finite difference stencil algorithm based on alternate tiling. Furthermore, an iteration space parallel two-way finite difference stencil algorithm based on alternate tiling was proposed, introducing the sequence of iterative space tiles as the sequence of execution and using time skewing technique to partition the iteration space, thus to improve the data locality of algorithm. The cache misses and the cost of communication and synchronization are reduced by reordering the tiles of iteration space. Finally, the effectiveness of the two parallel SIMPLE algorithms were compared. The results showed that the parallel SIMPLE algorithm that uses the two-way finite difference stencil algorithm based on alternate tiling has good data locality, performance, and scalability in the Deepcomp7000 cluster computing environment. Copyright © 2013 John Wiley & Sons, Ltd.

Multiagent systems are commonly used for simulation of new paradigms of energy distribution. Especially when considering Smart Grids, the autonomicity deployed by goal-driven agents implies the need for being aware of multiple aspects connected to the energy distribution context. With ‘context’, we refer to the outside world variables such as weather, stock market trends, location of the users, government actions, and so on; therefore, an architecture highly context-aware is needed. We propose a model in which every important factor concerning the electric energy distribution is presented by modeling context-aware agents able to identify the impact of these factors. Moreover, some tests have been performed regarding the web service integration in which agents contracting energy will automatically retrieve data to be used in adaptive and collaborative aspects; an explicative example is represented by the retrieval of weather forecasting that provides input on ongoing demand and data for the predicted availability (in case of photovoltaic or wind powered environments). Copyright © 2013 John Wiley & Sons, Ltd.

Social Clouds provide the capability to share resources among participants within a social network—leveraging on the trust relationships already existing between such participants. In such a system, users are able to trade resources between each other rather than make use of capability offered at a (centralized) data center. Although such an environment has significant potential for improving resource utilization and making available additional capacity that remains dormant, incentives for sharing remain an important hurdle limiting its effective. In this paper, we utilize the socioeconomic model proposed by Silvio Gesell to demonstrate how a ‘virtual currency’ can be used to incentivise sharing of resources within a ‘community’. We subsequently demonstrate, through simulations, the benefit provided to participants within such a community using a variety of economic (such as overall credits gained) and technical (number of successfully completed transactions) metrics. Further, we describe our implementation of such a Social Cloud using CometCloud. CometCloud is an autonomic computing engine for cloud and grid environments. It supports highly heterogeneous and dynamic federated cloud/Grid infrastructures, integration of public/private clouds and autonomic cloudbursts. We demonstrate the implementation of two designs on the basis of the master/worker approach: (i) one tuple space per cluster and (ii) one coordination tuple space and multiple transient spaces—one per each cluster. Finally, we discuss an extended version of our Social Cloud model where intermediary relay nodes take on more active roles as traders in a transaction. Copyright © 2013 John Wiley & Sons, Ltd.

The writing of efficient parallel code has always been a tedious and time-consuming process. However, the performance prediction prototype tools devised in the last decade come of age now, thanks to the availability of the almost unlimited computing power of clouds. This paper presents the practical use of mJADES, a novel environment for running multiple concurrent simulations in the cloud, to predict the performance of parallel code in multiple working conditions at once. After an introduction on mJADES and its operational aspects, the construction of parallel code performance prediction models will be dealt with. The models and the results obtained for a simple but complete and meaningful case study will be presented, discussing the accuracy obtained by simulation and the time required for performing the whole set of simulations necessary to characterize the program behavior. Copyright © 2013 John Wiley & Sons, Ltd.

Effective scheduling is a key concern for the execution of performance-driven grid applications such as workflows. In this paper, we first define the workflow scheduling problem and describe the existing heuristic-based and metaheuristic-based workflow scheduling strategies in grids. Then, we propose a dynamic critical-path-based adaptive workflow scheduling algorithm for grids, which determines efficient mapping of workflow tasks to grid resources dynamically by calculating the critical path in the workflow task graph at every step. Using simulation, we compared the performance of the proposed approach with the existing approaches, discussed in this paper for different types and sizes of workflows. The results demonstrate that the heuristic-based scheduling techniques can adapt to the dynamic nature of resource and avoid performance degradation in dynamically changing grid environments. Finally, we outline a hybrid heuristic combining the features of the proposed adaptive scheduling technique with metaheuristics for optimizing execution cost and time as well as meeting the users requirements to efficiently manage the dynamism and heterogeneity of the hybrid cloud environment. Copyright © 2013 John Wiley & Sons, Ltd.

This paper presents a parallel algorithm for constructing Voronoi diagrams based on point-set adaptive grouping. The binary tree splitting method is used to adaptively group the point set in the plane and construct sub-Voronoi diagrams for each group. Given that the construction of Voronoi diagrams in each group consumes the majority of time and that construction within one group does not affect that in other groups, the use of a parallel algorithm is suitable. After constructing the sub-Voronoi diagrams, we extracted the boundary points of the four sides of each sub-group and used to construct boundary site Voronoi diagrams. Finally, the sub-Voronoi diagrams containing each boundary point are merged with the corresponding boundary site Voronoi diagrams. This produces the desired Voronoi diagram. Experiments demonstrate the efficiency of this parallel algorithm, and its time complexity is calculated as a function of the size of the point set, the number of processors, the average number of points in each block, and the number of boundary points. Copyright © 2013 John Wiley & Sons, Ltd.

Association relations between concepts are a class of simple but powerful regularities in binary data, which play important roles in enterprises and organizations with huge amounts of data. However, although there can be easily large number of association relation mined from databases, since existing objective and subjective methods scarcely take semantics into consideration, it has been recognized early in the knowledge discovery literature that most of them are of no interest to the user. In this paper, the semantic discrimination capability (SDC) of association relation is measured based on discrimination value model first. The formula of SDC integrating both statistical and graph features is proposed from five different strategies. The high correlation coefficient of the proposed method against discrimination value shows that the proposed SDC measure is accuracy. Moreover, an application using SDC on document clustering is carried out, which shows that SDC has broad prospects on data-related task such as document clustering. Copyright 2013 John Wiley © Sons, Ltd.

The employment of batch processing in workflow is to model and schedule activity instances in multiple workflow cases of the same workflow type to optimize business processes execution dynamically. Although our previous works have preliminarily investigated its model and implementation, it is still necessary to deal with its model design problem. Process mining techniques allow for the automated discovery of process models from event logs and have received notable attentions in researches recently. Following these researches, this paper proposes an approach to mine batch processing workflow models from event logs by considering the batch processing relations among activity instances in multiple workflow cases. The notion of batch processing feature and its corresponding mining algorithm are also presented for discovering the batch processing area in the model by using the input and output data information of activity instances in events. The algorithms presented in this paper can help to enhance the applicability of existing process mining approaches and broaden the process mining spectrum. Copyright © 2013 John Wiley & Sons, Ltd.

Traditionally, service discovery is often promoted by the centralized approach that typically suffers from single point of failure, poor reliability, poor scalability, to name a few. In view of this challenge, a QoS-aware service discovery method is investigated for elastic cloud computing in an unstructured peer-to-peer network in this paper. Concretely speaking, the method is deployed by two phases, that is, service registering phase and service discovery phase. More specifically, for a peer node engaged in the unstructured peer-to-peer network, it firstly registers its functional and nonfunctional information to its neighbors in a flooding way. With the multiple registered information, the QoS-aware service discovery is promoted in a probabilistic flooding way according to the network traffic. At last, extensive simulations are conducted to evaluate the feasibility of our method. Copyright © 2013 John Wiley & Sons, Ltd.

Composite service selection is one of the core research issues in Web service composition. Because of the complex service correlation context, candidate services may perform differently when being used with other services. Presently, most service selection approaches ignore this issue, which makes the selected composite services less efficient than expected. To solve this problem, a service correlation context-aware composite service selection approach is proposed on the basis of the concept of single-entry single-exit (SESE) region. The general process of our approach is as follows: (1) mining the SESE patterns that are frequently used together in the set of efficiently executed instances of a composite service; (2) dividing the process model of the composite service into SESE regions and generating the candidate SESE pattern set of each region, using the discovered SESE pattern set; and (3) optimizing composite service selection globally on the basis of QoS using divided regions as selection units and their candidate pattern sets as candidate service sets. Because SESE patterns are testified by large amount of efficiently executed instances, they have higher quality than the results of independent selection of services in an SESE region. Experimental results demonstrated that our approach can improve the quality of selected composite services effectively in the correlation context. Concurrency and Computation: Practice and Experience, 2012.© 2013 Wiley Periodicals, Inc.

Computer simulations have become an indispensable tool for the empirical study of large-scale systems. The timely simulation of these systems, however, is not without its challenges. Simulators have to be able to harness the full computational power of modern multicore architectures through parallel execution and overcome the memory limitations of a single computer. In this paper, we evaluate the performance of a parallel and distributed simulator using several conventional time synchronization protocols executed on modern multicore hardware. In addition, we comprehensively analyze a hybrid approach, combining two traditional protocols, increasing robustness, and enabling improved performance in a wider range of simulation scenarios. Finally, an adaptive algorithm to automatically configure this hybrid protocol is introduced and evaluated, eliminating manual user intervention and further improving robustness with respect to varying simulation conditions.Copyright © 2013 John Wiley & Sons, Ltd.

Designing and implementing an interoperable and flexible service process collaboration strategy is one of key issues for business to business integrations. To better support service process collaboration, an event view model is proposed, which is composed of a set of event types and their dependency relationships. It provides a general and flexible way to define a public view of a service process model and serves as the basis for defining service process collaboration protocols. In the paper, the basic concepts and a system framework for event-based service process collaboration are first introduced. The definitions of event and the dependency relationships among event types are then presented. Especially, how to identify dependency relationships among composite event types is studied in detail. After discussing the definition of event view and its specifying approach, a procedure for transforming a BPEL process model into an event model and deriving dependencies among events is given. Finally, a case study is presented, and some implementation issues for defining and publishing an event view are discussed. Copyright © 2013 John Wiley & Sons, Ltd.

The mobile agent technique has been broadly used in next generation distributed systems. The system performance measurement and simulation are required before the system can be deployed on a large scale. In this paper, we address performance analysis on a finite state mobile agent prototype on the basis of Virtual Hierarchical Tree Grid Organizations (VIRGO). The finite states refer to the migration, execution, and searching of the mobile agent. We introduce a novel evaluation model for the finite state mobile agent. The experimental results based on this evaluation model show that the finite mobile agents can perform well under multiple agent conditions and are superior to the traditional client/server approach.. Copyright © 2013 John Wiley & Sons, Ltd.

Resource provisioning is one of the main challenges in large-scale distributed systems such as federated Grids. Recently, many resource management systems in these environments have started to use the lease abstraction and virtual machines (VMs) for resource provisioning. In the large-scale distributed systems, resource providers serve requests from external users along with their own local users. The problem arises when there is not sufficient resources for local users, who have higher priority than external ones, and need resources urgently. This problem could be solved by preempting VM-based leases from external users and allocating them to the local ones. However, preempting VM-based leases entails side effects in terms of overhead time as well as increasing makespan of external requests. In this paper, we model the overhead of preempting VMs. Then, to reduce the impact of these side effects, we propose and compare several policies that determine the proper set of lease(s) for preemption. We evaluate the proposed policies through simulation as well as real experimentation in the context of InterGrid under different working conditions. Evaluation results demonstrate that the proposed preemption policies serve up to 72% more local requests without increasing the rejection ratio of external requests. Copyright © 2013 John Wiley & Sons, Ltd.

Concurrent programming has become a common means to harness the potential performance of multi-core processors. System V (SysV) message queues and semaphores have been used since the mid 1970s to implement inter-process concurrency, but they are difficult to use, and bindings exist for few programming languages. This paper introduces ipcmd, a high-level command-line interface to SysV message queues and semaphores. ipcmd provides an easy-to-use interface for synchronizing concurrent processes to allow application developers to efficiently prototype, debug, and test the use of SysV semaphores and message queues in applications. Easy-to-understand applications of semaphores are illustrated using simple shell scripts.Copyright © 2013 John Wiley & Sons, Ltd.

As enterprises turning to SOA, services-oriented business ecosystem (SOBE) has become an important pattern for the organization and management of the massive business services. At the same time, the emergence of Internet of Services (IOS) provides a business model in which service vendors and consumers can interact with each other via the Internet. This paradigm makes it possible that the services in SOBE are managed in an autonomous and coordinated manner. The challenge here is to organize these massive business services, coordinate, and federate them to achieve the benefits of SOA. To address these challenges, this paper presents BSNet, a framework on the basis of the service correlation networks to manage the SOBE. The model consists of a who-what-how service correlation network that captures the various relations in SOBE. Finally, a prototype system is developed, and a simulated case study is provided to show the expanded value of our network-based framework. Copyright © 2013 John Wiley & Sons, Ltd.

Aggregate queries are useful tools in the context of sensor network-based systems as they retrieve knowledge from huge amounts of summarized readings to be exploited for knowledge discovery purposes. Actually, data representation and query models are problematic issues for managing sensor network data, because streams produced by sensors are theoretically unbounded. In this paper, we present a Grid framework, called SensorGrid, on the basis of data compression and approximation paradigms, which allows us to provide approximate answers to aggregate queries on summarized sensor network data. These queries are the basis for achieving Online Analytical Processing (OLAP) over sensor network readings in Data Grid environments, with both effectiveness and efficiency. We also present our experience in the context of a real-life system focused on the management of environmental sensor network data. Another contribution of our research is represented by the extensive experimental evaluation and analysis of SensorGrid, which, in more details, focuses on two main classes of aggregate range queries over sensor readings, namely, (i) the window queries, which apply an SQL aggregation operator over a fixed window over the reading stream produced by the sensor network, and (ii) the continuous queries, which instead consider a ‘moving’ window and produce as output a stream of answers. Both classes of queries are extremely useful to extract summarized knowledge to be exploited by OLAP-like analysis tools over sensor network data. The experimental results, conducted on both synthetic and real-life data sets, clearly confirm the benefits deriving from embedding data compression and approximation paradigms into Grid-based sensor network data-intensive management systems.Copyright © 2013 John Wiley & Sons, Ltd.

Workflow scheduling has been extensively studied to improve the system performance. However, existing approaches are usually built on predefined workflow graph structure, neglecting the possibility that a workflow graph itself may be changeable when certain conditions are satisfied. Therefore, in this paper, we propose the concept of graph refactoring that transforms certain types of sequential tasks to run in parallel without changing system's functionality. We first provide a classification for task dependencies in workflows and identify that previously sequential task ordering in loose control dependency can be scheduled to run in parallel as long as supporting services are trustworthy. With this concept, we present a refactoring algorithm to traverse, restructure, and parallelize loose control dependencies in the graph when the reputations of related executing services are above certain threshold. In addition, refactoring effects on common sub-graph structures are analyzed and discussed. In practice, our algorithm can be integrated into existing workflow management systems as a preprocessor to generate a new functionally equivalent working graph with more concurrent branches for further scheduling. Experiments and analysis show that graph refactoring can improve the system performance scalably because of concurrent execution of previously sequential tasks. Copyright © 2013 John Wiley & Sons, Ltd.

Virtual organization (VO) is a main organizational paradigm for enterprises to collaborate in the rapidly changing environment. However, finding partners for VO creation has great challenges on Internet, because a VO initiator is blinded without enough partners' information. The problem of VO creation in cloud environment is discussed. A VO creating algorithm (called Group-Choose) based on reputation system is presented to help initiator minimize the operating risk on Internet. In the algorithm, a VO initiator aggregates partners' trust evaluations of candidates to select new partners for VO, instead of evaluating candidate's trust only by itself. The third-party assessment and Time Slide Window are used in peer-to-peer trust evaluation to make the model more adaptive in the dynamic environment. The proposed method focuses on the reputation data, without taking other factors into consideration. Compared with PathTrust and PeerTrust, the Group-Choose algorithm for VO creation has better performance in resisting conspiracy attack and periodic attack. Copyright © 2013 John Wiley & Sons, Ltd.

Increasing demand for computationally efficient algorithms and processors has turned the attention of researchers toward parallel and concurrent solutions. Because the frequency of contemporary processors cannot be tweaked infinitely, the only hopes for squeezing more performance from computers are parallel processing and parallel computation. The important part of every parallel solution is concurrent data structures, which allow multithread programming environments to be taken advantage of. In this article, a new concurrent dynamic set structure is proposed. It is based on the van Emde Boas trees concept, where on every node of a tree, an array of the node's children is stored. The structure is equipped with a simple but effective locking algorithm, which allows it to be used concurrently by any number of threads. The presented algorithm idea is accompanied by an experimental implementation written in JAVA 6. Preliminary tests prove that, especially for moderately larger data sets with a predominance of read operations, the concurrent van Emde Boas array proposed in this article may be a viable alternative for other structures providing a similar functionality.Copyright © 2013 John Wiley & Sons, Ltd.

The database services on cloud are appearing as an attractive way of outsourcing databases. When a database is deployed on a cloud database service, the data security and privacy becomes a big concern for users. A straightforward way to address this concern is to encrypt the database. However, after encryption, the database cannot be easily queried. In this paper, we propose a nonlinear order preserving scheme for indexing encrypted data, which facilitates the range queries over encrypted databases. The scheme is secure even there are a large number of duplicates in plaintexts. Moreover, our scheme allows the programmability of basic indexing expressions and thus provides the capability of hiding the distribution of plaintexts from the distribution of indexes. This scheme is suitable for long-standing databases because its use does not need any assumption on the characteristics of database data, such as their distribution, range and number, which may change dramatically over time.Copyright © 2013 John Wiley & Sons, Ltd.

This paper discusses performance optimization on the dynamical core of global numerical weather prediction model in Global/Regional Assimilation and Prediction System (GRAPES). GRAPES is a new generation of numerical weather prediction system developed and currently used by Chinese Meteorology Administration. The computational performance of the dynamical core in GRAPES relies on the efficient solution of three-dimensional Helmholtz equations, which lead to large-scale and sparse linear systems formulated by the discretization in space and time. We choose generalized conjugate residual (GCR) algorithm to solve the corresponding linear systems and further propose algorithm optimizations for large-scale parallelism in two aspects: (i) reduction of iteration number for solution and (ii) performance enhancement of each GCR iteration. The reduction of iteration number is achieved by advanced preconditioning techniques, combining block incomplete LU factorization-k preconditioner over 7-diagonals of the coefficient matrix with the restricted additive Schwarz method effectively . The improvement for GCR iteration is to reduce the global communication operations by refactoring the GCR algorithm, which decreases the communication overhead over large number of cores. Performance evaluation on the Tianhe-1A system shows that the new preconditioning techniques reduce almost one-third iterations for solving the linear systems, the proposed methods can obtain 25% performance improvement on average compared with the original version of Helmholtz solver in GRAPES, and the speedup with our algorithms can reach 10 using 2048 cores compared with 256 cores.Copyright © 2013 John Wiley & Sons, Ltd.

New challenges for large-scale data management and sharing have arisen because of the information evolution from massive data to “big data”. In this paper, we propose the concept of virtual dataspaces and domain scientific data cloud (DSDC) to address the challenges of management, reusability, and service of scientific data in scientific domain. The application of DSDC is also introduced, which is used in the materials science domain for e-Science applications. Virtual dataspaces is then defined, which is used to integrate and organize both structured and unstructured scientific data in cloud environment. To build the virtual dataspaces, a logical resources model based on ontology is discussed. And then a new mapping and evolutionary model of virtual dataspaces is described in a “pay-as-you-go” fashion. As such, we introduce a “semi-automatic evolution” method. A scientific data cloud application model is also proposed for defining service template. Finally, the virtual dataspaces and DSDC are verified by “material service safety evaluation”, and the results show that our approach is efficient in scientific data management and suitable for data-intensive application. Copyright © 2012 John Wiley & Sons, Ltd.

Earth observation applications are now facing the challenges of managing and processing massive data sets from multiple sources from large-scale distributed data centers (DCs). To solve this research problem, this paper presents an infrastructure of multiple data centers (MDC) for managing and processing massive remote sensing images. The proposed system is built on both groups of distributed DCs/clusters, which are equipped with DC or cluster resource manager. Access security and information service are introduced to support this architecture of MDC. We collaboratively organized the algorithm, and data belonged to the MDC in the manner of workflow. In practice, we succeeded in working out the concrete problems regarding procedures in processing applications collaboratively and transfer the massive remote sensing dataset fast and with stable cross-MDC. On the basis of the previously mentioned research work, we will investigate the platform integration of MDC. Copyright © 2012 John Wiley & Sons, Ltd.

Computational grids allow the sharing of geographically distributed computational resources in an efficient, reliable, and secure manner. Grid is still in its infancy, and there are many problems associated with the computational grid, namely job scheduling, resource management, information service, information security, routing, fault tolerance, and many more. Scheduling of jobs on grid nodes is an NP-class problem warranting for heuristic and meta-heuristic solution approach. In the proposed work, a meta-heuristic technique, auto controlled ant colony optimization, has been applied to solve this problem. The work observes the effect of interprocess communication in process to optimize turnaround time of the job. The proposed model has been simulated in Matlab. For the different scenarios in computational grid, results have been analyzed. Result of the proposed model is compared with another meta-heuristic technique genetic algorithm that has been applied for the same purpose. It is found that auto controlled ant colony optimization not only gives better solution in comparison to genetic algorithm, but also converges faster because initial solution itself is good because of constructive and decision-based policy adapted by the former. Concurrency and Computation: Practice and Experience, 2012.© 2012 Wiley Periodicals, Inc.

Because of the large datasets that are usually involved in deoxyribonucleic acid (DNA) sequence alignment, the use of optimal local alignment algorithms (e.g., Smith–Waterman) is often unfeasible in practical applications. As such, more efficient solutions that rely on indexed search procedures are often preferred to significantly reduce the time to obtain such alignments. Some data structures that are usually adopted to build such indexes are suffix trees, suffix arrays, and the hash tables of q-mers.

This paper presents a comparative analysis of highly optimized parallel implementations of index-based search algorithms using these three distinct data structures, considering two different parallel platforms: a homogeneous multi-core central processing unit (CPU) and a NVidia Fermi graphics processing unit (GPU). Contrasting to what happens with CPU implementations, the obtained experimental results reveal that GPU implementations clearly favor the suffix arrays, because of the achieved performance in terms of memory accesses. Furthermore, the results also reveal that both the suffix trees and suffix arrays outperform the hash tables of q-mers when dealing with the largest datasets.

When compared with a quad-core CPU, the results demonstrate the possibility to achieve speedups as high as 65 with the GPU when considering a suffix-array index, thus making it an adequate choice for high-performance bioinfomatics applications.Copyright © 2012 John Wiley & Sons, Ltd.

In the design process of computer systems or processor architectures, typically many different parameters are exposed to configure, tune, and optimize every component of a system. For evaluations and before production, it is desirable to know the best setting for all parameters. Processing speed is no longer the only objective that needs to be optimized; power consumption, area, and so on have become very important. Thus, the best configurations have to be found in respect to multiple objectives. In this article, we use a multi-objective design space exploration tool called Framework for Automatic Design Space Exploration (FADSE) to automatically find near-optimal configurations in the vast design space of a processor architecture together with a tool for code optimizations and hence evaluate both automatically. As example, we use the Grid ALU Processor (GAP) and its postlink optimizer called GAPtimize, which can apply feedback-directed and platform-specific code optimizations. Our results show that FADSE is able to cope with both design spaces. Less than 25% of the maximal reasonable hardware effort for the scalable elements of the GAP is enough to achieve the processor's performance maximum. With a performance reduction tolerance of 10%, the necessary hardware complexity can be further reduced by about two-thirds. The found high-quality configurations are analyzed, exhibiting strong relationships between the parameters of the GAP, the distribution of complexity, and the total performance. These performance numbers can be improved by applying code optimizations concurrently to optimizing the hardware parameters. FADSE can find near-optimal configurations by effectively combining and selecting parameters for hardware and code optimizations in a short time. The maximum observed speedup is 15%. With the use of code optimizations, the maximum possible reduction of the hardware resources, while sustaining the same performance level, is 50%.Copyright © 2012 John Wiley & Sons, Ltd.

Data centers are experiencing a remarkable growth in the number of interconnected servers. Being one of the foremost data center design concerns, network infrastructure plays a pivotal role in the initial capital investment and ascertaining the performance parameters for the data center. Legacy data center network (DCN) infrastructure lacks the inherent capability to meet the data centers growth trend and aggregate bandwidth demands. Deployment of even the highest-end enterprise network equipment only delivers around 50% of the aggregate bandwidth at the edge of network. The vital challenges faced by the legacy DCN architecture trigger the need for new DCN architectures, to accommodate the growing demands of the ‘cloud computing’ paradigm. We have implemented and simulated the state of the art DCN models in this paper, namely: (a) legacy DCN architecture, (b) switch-based, and (c) hybrid models, and compared their effectiveness by monitoring the network: (a) throughput and (b) average packet delay. The presented analysis may be perceived as a background benchmarking study for the further research on the simulation and implementation of the DCN-customized topologies and customized addressing protocols in the large-scale data centers. We have performed extensive simulations under various network traffic patterns to ascertain the strengths and inadequacies of the different DCN architectures. Moreover, we provide a firm foundation for further research and enhancement in DCN architectures. Copyright © 2012 John Wiley & Sons, Ltd.

This article highlights the issue of upcoming wider single-instruction, multiple-data units as well as steadily increasing core counts on contemporary and future processor architectures. We present the recent port to and latest results of cache-oblivious algorithms and implementations of our TifaMMy code on four architectures: SGI's UltraViolet distributed shared-memory machine, Intel's latest x86 architecture code-named Sandy Bridge, AMD's new Bulldozer architecture, and Intel's future Many Integrated Core architecture. TifaMMy's matrix multiplication and LU decomposition routines have been adapted and tuned with regard to these architectures. Results are discussed and compared with vendors’ architecture-specific and optimized libraries, Math Kernel Library and AMD Core Math Library, for both a standard C++ version with vectorization compiler switches and TifaMMy's highly optimized vector intrinsics version. We provide insights into architectural properties and comment on the feasibility of heterogeneous cores and accelerators, namely graphics processing units. Besides bare-metal performance, the test platforms’ ease of use is analyzed in detail, and the portability of our approach to new and upcoming silicon is discussed with regard to required effort on code change abstraction levels.

As a result, we demonstrate that because of its generic structure in terms of memory organization, TifaMMy executes with equally efficient performance on all four architectures as it automatically adapts itself to architectural parameters without losing performance against the Math Kernel Library and AMD Core Math Library, underlining its generic and cache-oblivious properties, as the porting effort was relatively low compared with that in other implementations.Copyright © 2012 John Wiley & Sons, Ltd.

The rapid increase in the volume of data available on the Internet makes it increasingly impractical for a crawler to index the whole Web. Instead, many intelligent crawlers, known as ontology-based semantic focused crawlers, have been designed by making use of Semantic Web technologies for topic-centered Web information crawling. Ontologies, however, have constraints of validity and time, which may influence the performance of the crawlers. Ontology-learning-based focused crawlers are therefore designed to automatically evolve ontologies by integrating ontology learning technologies. Nevertheless, surveys indicate that the existing ontology-learning-based focused crawlers do not have the capability to automatically enrich the content of ontologies, which makes these crawlers unreliable in the open and heterogeneous Web environment. Hence, in this paper, we propose a framework for a novel semi-supervised ontology-learning-based focused (SOF) crawler, the SOF crawler, which embodies a series of schemas for ontology generation and Web information formatting, a semi-supervised ontology learning framework, and a hybrid Web page classification approach aggregated by a group of support vector machine models. A series of tests are implemented to evaluate the technical feasibility of this proposed framework. The conclusion and the future work are summarized in the final section.Copyright © 2012 John Wiley & Sons, Ltd.

This paper presents a novel Area Of Interest (AOI)-cast algorithm for distributed virtual environments targeted to Delaunay-based P2P overlays. The algorithm exploits the mathematical properties of Delaunay triangulations to build a spanning tree supporting the notification of the events generated by a peer to the other ones located in its AOI. The spanning tree is computed by the reversing compass routing, a routing algorithm proposed for geometric networks. Our approach presents a set of novel features. First, it requires only the knowledge of the peer's neighbors, so that the amount of traffic load on the P2P overlay is minimized. Second, we prove that, for circular shaped AOI, the algorithm builds a spanning tree covering all and only the peers of the AOI. Finally, our approach takes into account the possible inconsistencies among the local views of the peers, because the network latency, by introducing a tolerance threshold in the reverse compass routing. We present a set of simulations considering both synthetic data and real data traces taken from a real multiplayer game, which show the effectiveness of our proposal. Copyright © 2012 John Wiley & Sons, Ltd.

Utility computing is a form of computer service whereby the company providing the service charges the users for using the system resources. In this paper, we present system-optimal and user-optimal price-based job allocation schemes for utility computing systems whose objective is to minimize the cost for the users. The system-optimal scheme provides an allocation of jobs to the computing resources that minimizes the overall cost for executing all the jobs in the system. The user-optimal scheme provides an allocation that minimizes the cost for individual users in the system for providing fairness. The system-optimal scheme is formulated as a constraint minimization problem, and the user-optimal scheme is formulated as a non-cooperative game. The prices charged by the computing resource owners for executing the users jobs are obtained using a pricing model based on a non-cooperative bargaining game theory framework. The performance of the studied job allocation schemes is evaluated using simulations with various system loads and configurations. Copyright © 2012 John Wiley & Sons, Ltd.

This article studies the performance and scalability of a geometric multigrid solver implemented within the hierarchical hybrid grids (HHG) software package on current high performance computing clusters up to nearly 300,000 cores. HHG is based on unstructured tetrahedral finite elements that are regularly refined to obtain a block-structured computational grid. One challenge is the parallel mesh generation from an unstructured input grid that roughly approximates a human head within a 3D magnetic resonance imaging data set. This grid is then regularly refined to create the HHG grid hierarchy. As test platforms, a BlueGene/P cluster located at Jülich supercomputing center and an Intel Xeon 5650 cluster located at the local computing center in Erlangen are chosen. To estimate the quality of our implementation and to predict runtime for the multigrid solver, a detailed performance and communication model is developed and used to evaluate the measured single node performance, as well as weak and strong scaling experiments on both clusters. Thus, for a given problem size, one can predict the number of compute nodes that minimize the overall runtime of the multigrid solver. Overall, HHG scales up to the full machines, where the biggest linear system solved on Jugene had more than one trillion unknowns. Copyright © 2012 John Wiley & Sons, Ltd.

Transactional memory is a promising technique for enforcing disciplined access to shared data in a multiprocessor system. Transactional memory simplifies the implementation of a variety of concurrent data structures. In this paper, we study the benefits of a modest, real-time aware, hardware implementation of transactional memory that we call micro-transactions. In particular, we argue that hardware support for micro-transactions allows us to efficiently implement certain data structures. Those data structures are difficult to realize with the atomic operations provided by stock hardware and provide real-time guarantees for those operations. Our main implementation platform is the Java Optimized Processor system, a field-programmable gate array (FPGA) implementation of the Java virtual machine, optimized for real-time Java. We report on the performance of data structures implemented with locks, atomic instructions, and micro-transactions. Our results suggest that transactional memory is an interesting alternative to traditional concurrency control mechanisms.Copyright © 2012 John Wiley & Sons, Ltd.

Multicore processor systems have become mainstream. To release the full potential of multiple cores, applications are programmed to be parallel to keep every core busy. Unfortunately, lock contention within operating systems can limit the scalability so seriously that use of more cores leads to reduced throughput (scalability collapse). To understand and characterize the collapse behavior easily, a discrete-event simulation model, which considers both the sequential execution of critical sections and the overhead of hardware resource contention, is designed and implemented. By the use of the model, we observe that the percentage of time used to wait for locks and the number of tasks requesting for a lock have a significant correlation with the occurrence of scalability collapse. On the basis of these observations, two new techniques (lock contention aware scheduler and requester-based adaptive lock) are proposed to remove the scalability collapse on multicores. The proposed methods are implemented in the Linux kernel 2.6.29.4 and evaluated on an AMD 32-core system to verify their effectiveness. By using micro-benchmarks and macro-benchmarks, we find that these methods can remove scalability collapse totally for four of five workloads exhibiting the collapse behavior. For one workload that does not suffer scalability collapse, these proposed methods only introduce negligible overhead. Copyright © 2012 John Wiley & Sons, Ltd.

Over the last decade, the popularity of Python has increased considerably. Python is widely used and has been demonstrated to be effective over many problem domains including scripting, prototyping, and simulation. Python's easy to use and concise syntax is highly expressive and allows a developer to create considerably shorter and easier to understand programs than semantically equivalent programs written in languages like C, C++, or Java. An important aspect of any language's flexibility is a highly parallelizable environment that allows its users to write concurrent programs. However, Python is still lacking a high-level, expressive concurrent and distributed programming environment. This paper presents our experience creating PySy, a Python package (on the basis of the SR and JR concurrent programming languages), which provides an easy to use and expressive concurrent environment and allows for the distributed sharing of resources. This paper discusses our design decisions, describes our implementation, and shows qualitative and quantitative analyses of well-known concurrent programs written in PySy. Overall, PySy is reasonable to use for expressing the more common programming scenarios, while it provides acceptable performance. Copyright © 2012 John Wiley & Sons, Ltd.

There is an increasing number of high-performance periodic real-time applications in areas such as control systems, autonomous robots and financial systems. This article presents a novel algorithm, called Notional Approximation for Balancing Load Residues (NABLR), for scheduling these applications on high-performance computing resources. The algorithm utilizes a combination of task residual loads and runtime laxities to carefully plan task execution between two consecutive job arrivals, so that available resources can be fully utilized and avoid deadline misses as possible. The empirical study in our article presented at the 2011 International Conference on High Performance Computing and Simulation (HPCS) was further extended by including additional static task sets and a new adaptive task set generated by our motivating application in brain–machine interfaces, which simulates the control of movement of a prosthetic limb according to activities of input signals. Out of 25,000 task sets, NABLR can schedule up to 76% of the sets versus 43% by the best known efficient algorithm (named anticipating slack earliest deadline first until zero laxity [ASEDZL]), while incurring significantly smaller overheads than those of a known optimal algorithm (on average, 80% fewer preemptions, migrations, and 75% fewer scheduler invocations), and being comparable to those of suboptimal schedulers (within only 12% more preemptions/migrations). Additionally, the evaluation results show that NABLR completes more task instances when compared with ASEDZL, which yields a greater system output accuracy. Copyright © 2012 John Wiley & Sons, Ltd.

During the last decade, the number of distributed application domains with temporal requirements has significantly augmented, arising the necessity of exploring new concepts and paradigms that allow, on the one hand, the development of dynamic and flexible distributed applications and, on the other hand, the reusability of code. Service-oriented paradigms have been successfully applied to distributed environments, increasing their flexibility and allowing the reusability of their components. Besides, distributed real-time Java technologies have shown to be a good candidate to deploy real-time distributed applications. This paper presents a model for service-oriented applications on a time-triggered distributed real-time Java environment, focusing on the definition of the temporal model of an application and its schedulability, applying and evaluating this model in real-time service-oriented composition algorithms.Copyright © 2012 John Wiley & Sons, Ltd.

This paper describes a new fast and scalable parallel algorithm to automatically determine catchment basin of rivers in large digital elevation models (DEMs). This algorithm is based on the construction of a minimal spanning tree, via a hierarchy of graphs, modeling the water route on the DEM. It combines different techniques used in hydrogeology, image processing and graph theory to obtain the most accurate results in terms of geomorphology without any preprocessing. The method tends to exploit the most of the DEMs, avoiding misleading inconsistencies DEMs contain. It has been designed to be entirely parallel and scalable for architectures such as PC clusters. Some experiments are presented to show accuracy, efficiency and scalability on huge DEMs. Copyright © 2012 John Wiley & Sons, Ltd.

Although modern computer hardware offers an increasing number of processing elements organized in nonuniform memory access (NUMA) architectures, prevailing middleware engines for executing business processes, workflows, and Web service compositions have not been optimized for properly exploiting the abundant processing resources of such machines. Amongst others, factors limiting performance are inefficient thread scheduling by the operating system, which can result in suboptimal use of system memory and CPU caches, and sequential code sections that cannot take advantage of multiple available cores.

In this article, we study the performance of the JOpera process execution engine on recent multicore machines. We first evaluate its performance without any dedicated optimization for multicore hardware, showing that additional cores do not significantly improve performance, although the engine has a multithreaded design. Therefore, we apply optimizations on the basis of replication together with an improved, hardware-aware usage of the underlying resources such as NUMA nodes and CPU caches. Thanks to our optimizations, we achieve speedups from a factor of 2 up to a factor of 20 (depending on the target machine) when compared with a baseline execution ‘as is’. Copyright © 2012 John Wiley & Sons, Ltd.

The number of providers in the cloud computing market is increasing at a rapid pace. They offer a wide range of pricing schemes, different types of instance, or even different value-added features to differ from other competitors, making the cloud market more complex. Cloud brokering enable users to choose the best cloud provider for their needs and avoid particular providers' lock in. Moreover, the use of multiple clouds offers several benefits such as scalability of services, improvement in fault tolerance, or cost reduction. However, users still find difficult the decision of where to deploy their resources as they have to handle too much information. The use of cloud brokering mechanisms is useful to reduce the complexity of using a multi-cloud environment. In this paper, we show a cloud broker architecture for deploying virtualized servers across available clouds. This architecture uses a scheduling module to obtain an optimal placement of a virtual infrastructure while making it transparent for users. We focus our investigation on dynamic cloud scenarios in terms of variable resource prices, taking into account the virtual machine migration overhead issue. We evaluate the performance of several use cases applied in real scenarios, and we show the improvement potential of using brokering mechanisms in dynamic deployments compared with static ones. Copyright © 2012 John Wiley & Sons, Ltd.

The challenge for Sparse Matrix–Vector multiplication (SpMV) performance is memory bandwidth, which mostly depends on input matrices and underlying computing platforms. To solve this challenge, many researchers have explored a variety of optimization techniques. One of the most promising aspects focuses on designing storage formats to represent sparse matrices. However, lots of prior storage formats cannot fully take advantage of the underlying computing platforms, resulting in unsatisfactory performance and large memory footprint. Therefore, a novel storage format, called Segmented Hybrid ELL + Compressed Sparse Row (CSR) (SHEC for short), is proposed to further improve the throughput and lessen memory footprint on Graphics Processing Unit (GPU). SHEC format employs an interleaved combination pattern, which combines certain amount of compressed rows to form a new SHEC row. Segmentation is brought in to balance load and reduce memory footprint. According to the empirical data, an automatic SHEC-based SpMV is developed to fit for all the matrices. Experimental results show that SHEC approach outperforms the best results of NVIDIA SpMV library and exhibits a comparable performance with state-of-the-art storage formats on the standard dataset. Copyright © 2012 John Wiley & Sons, Ltd.

This paper describes the GridWay metascheduler and exposes its latest and future developments, mainly related to interoperability and interoperation. GridWay enables large-scale, reliable, and efficient sharing of computing resources over grid middleware. To favor interoperability, it shows a modular architecture based on drivers, which access middleware services for resource discovery and monitoring, job execution and management, and file transfer. This paper presents two new execution drivers for Basic Execution Service (BES) and Computing Resource Execution and Management (CREAM) services and introduces a remote BES interface for GridWay. This interface allows users to access GridWay's job metascheduling capabilities, using the BES implementation of GridSAM. Thus, GridWay now provides to end users more possibilities of interoperability and interoperation.Copyright © 2012 John Wiley & Sons, Ltd.

The remotely sensed images continuously acquired by satellite and airborne sensors are increasing dramatically. Remote sensing applications are overwhelmed with tons of remote sensing data with complex data structures. Efficient programming in parallel systems for data-intensive applications like massive remote sensing data processing will be a challenge. We propose a generic data-structure oriented programming template to support massive remote sensing data processing in high-performance clusters. These templates provide distributed abstractions for large remote sensing image data with complex data structure and allow these distributed data to be accessed as a global one. Through data serialization and one-sided message passing primitives provided by message passing interface, the distributed remote sensing data template whose sliced data blocks are scattered among nodes could offer a simple and effective way to distribute and communicate massive remote sensing data. Efficient parallel input/output directly to and from the distributed data structure will also be offered to address the input/output bottleneck caused by massive image data. Developers can take the advantage of our templates to program efficient parallel remote sensing algorithms without dealing with data slicing and communication through low-level message passing interface APIs. Through experiments on remote sensing applications, we confirmed that our templates were productive and efficient. Copyright © 2012 John Wiley & Sons, Ltd.

Network optimization concerned with operational traffic management in existing data networks is typically oriented towards either maximizing throughput in congested networks while providing for adequate transmission quality, or towards balancing the traffic so as to maintain possibly large free capacity for carrying additional (new) traffic. Nowadays, the reduction of power consumption is a new key aspect in the development of modern wired networks. Power management capabilities allow modulating the energy consumption of devices that form a network by putting them into standby state, or by decreasing their performance in case of low incoming traffic volume. This paper presents a framework for backbone network management, which leads to the minimization of the energy used by this network. The policy for dynamic power management of the whole network through energy-aware routing, traffic engineering, and network equipment activity control is introduced and discussed. The concept of the system is to achieve the desired trade-off between total power consumption and the network performance according to the current load, incoming traffic, and user requirements. The effectiveness of our framework is illustrated by means of a numerical study. Copyright © 2012 John Wiley & Sons, Ltd.

Programmable graphics processing units (GPUs) nowadays offer tremendous computational resources for diverse applications. In this paper, we present the implementation of the dynamics routine of the HIRLAM weather forecast model on the NVIDIA GTX 480. The original Fortran code has been converted manually to C and CUDA. Empirically, it is determined what the optimal number of grid points per thread is, and what the best thread and block structures are. A significant amount of the elapsed time consists of transferring data between CPU and GPU. To reduce the impact of these transfer costs, we overlap calculation and transfer of data using multiple CUDA streams. We developed an algorithm that enables our code generator CTADEL to generate automatically the optimal CUDA streams program. Experiments are performed to find out if the applicability of GPUs is useful for Numerical Weather Prediction, in particular for the dynamics part.Copyright © 2012 John Wiley & Sons, Ltd.

The popularity of multimedia applications made them a major theme in embedded systems. The key component for supporting multimedia application well is embedded processor. Thus, we have designed and implemented an embedded processor, called UniDual processor, to achieve this objective. Its key features are the integration of instructions of reduced instruction set computers (RISCs) and digital signal processors (DSPs) as well as the support of special instruction set and shared-based clustered register architecture. However, an important issue of UniDual that remains open is how to efficiently allocate registers.

In this paper, we present a scheduling and instruction transformation approach to resolve the aforementioned issue. The proposed approach schedules instructions and then transforms overlapped instructions into RISC and DSP instructions by taking communication overhead and hardware limitations into account. Compared with the greedy approach, the evaluation shows that our work is relatively effective in performance and code size reduction.Copyright © 2012 John Wiley & Sons, Ltd.

This work presents a genetic algorithm (GA)-based optimization technique, called GA-ParFnt, to find the Pareto frontier for optimizing data transfer versus job execution time in grids. As the performance of a generic GA is not suitable to find such Pareto relationship, major modifications are applied to it so that it can efficiently discover such relationship. The frontier curve representing this relationship is then matched against performance of several scheduling techniques—for both data intensive and computationally intensive applications—to measure their overall performances. Results show that few of these algorithms are far from the Pareto front despite their claims of being efficient in optimizing their targeted objectives. Results also provide invaluable insights into this formidable problem and should aid in the design of future schedulers. Copyright © 2012 John Wiley & Sons, Ltd.

In recent years, more and more companies outsource their data to the cloud service provider to greatly reduce the cost. However, it also raises underlying security and privacy issues for the significant corporate data. Therefore, a natural way to keep sensitive data confidential against an untrusted cloud service provider is only to store the encrypted data in the cloud. Flexible encryption schemes can provide a fine grain access control for the encrypted data and ensure legitimate user to decrypt the corresponding data. The key problems of this approach include establishing access control for the encrypted data and revoking the access rights from users when they are no longer authorized to access the encrypted data on cloud servers. This paper aims to solve these problems. First, with the attribute encryption and the dual encryption system, we propose a concrete access control scheme constructed over the composite-order bilinear groups, and we prove its security under the standard model. Then, we propose a fully fine-grained revocation scheme under the direct revocation model so as to efficiently revoke access rights from users on cloud servers. Copyright © 2012 John Wiley & Sons, Ltd.

Scientific applications represent a dominant sector of compute-intensive applications. Using massively parallel processing systems increases the feasibility to automate such applications because of the cooperation among multiple processors to perform the designated task. This paper proposes a parallel hidden Markov model (HMM) algorithm for 3D magnetic resonance image brain segmentation using two approaches. In the first approach, a hierarchical/multilevel parallel technique is used to achieve high performance for the running algorithm. This approach can speed up the computation process up to 7.8× compared with a serial run. The second approach is orthogonal to the first and tries to help in obtaining a minimum error for 3D magnetic resonance image brain segmentation using multiple processes with different randomization paths for cooperative fast minimum error convergence. This approach achieves minimum error level for HMM training not achievable by the serial HMM training on a single node. Then both approaches are combined to achieve both high accuracy and high performance simultaneously. For 768 processing nodes of a Blue Gene system, the combined approach, which uses both methods cooperatively, can achieve high-accuracy HMM parameters with 98% of the error level and 2.6× speedup compared with the pure accuracy-oriented approach alone. Copyright © 2012 John Wiley & Sons, Ltd.

Confidence in a pairwise local sequence alignment is a fundamental problem in bioinformatics. For huge DNA sequences, this problem is highly compute-intensive because it involves evaluating hundreds of local alignments to construct an empirical score distribution. Recent parallel solutions support only kilobyte-scale sequence sizes and/or are based on sophisticated infrastructures that are not available for most of the research labs. This paper presents an efficient parallel solution for evaluating the statistical significance for a pair of huge DNA sequences using cloud infrastructures. This solution can receive requests from various researchers via web-portal and allocate resources according to their demand. In this way, the benefits of cloud-based services can be achieved. The fundamental innovation of this research work is proposing an efficient solution that utilizes both shared and distributed memory architectures via cloud technology to enhance the performance of evaluating the statistical significance for pair of DNA sequences. Therefore, the restriction on the sequence sizes is released to be in megabyte-scale, which was not supported before for the statistical significance problem. The performance evaluation of the proposed solution was carried out on Microsoft's cloud and compared with the existing parallel solutions. The results show that the processing speed outperforms the recent cluster solutions that target the same problem. In addition, the performance metrics exhibit linear behavior for the addressed number of instances. Copyright © 2012 John Wiley & Sons, Ltd.

Performance improvements in biomolecular simulations based on molecular dynamics (MD) codes are widely desired. Unfortunately, the factors, which allowed past performance improvements, particularly the microprocessor clock frequencies, are no longer increasing. Hence, novel software and hardware solutions are being explored for accelerating performance of widely used MD codes. In this paper, we describe our efforts on porting, optimizing and tuning of Large-scale Atomic/Molecular Massively Parallel Simulator, a popular MD framework, on heterogeneous architectures: multi-core processors with graphical processing unit (GPU) accelerators. Our implementation is based on accelerating the most computationally expensive non-bonded interaction terms on the GPUs and overlapping the computation on the CPU and GPUs. This functionality is built on top of message passing interface that allows multi-level parallelism to be extracted even at the workstation level with the multi-core CPUs and allows extension of the implementation on GPU-enabled clusters. We hypothesize that the optimal benefit of heterogeneous architectures for applications will come by utilizing all possible resources (for example, CPU-cores and GPU devices on GPU-enabled clusters). Benchmarks for a range of biomolecular system sizes are provided, and an analysis is performed on four generations of NVIDIA's GPU devices. On GPU-enabled Linux clusters, by overlapping and pipelining computation and communication, we observe up to 10-folds application acceleration in multi-core and multi-GPU environments illustrating significant performance improvements. Detailed analysis of the implementation is presented that allows identification of bottlenecks in algorithm, indicating that code optimization and improvements on GPUs could allow microsecond scale simulation throughput on workstations and inexpensive GPU clusters, putting widely desired biologically relevant simulation time-scales within reach of a large user community. In order to systematically optimize simulation throughput and to enable performance prediction, we have developed a parameterized performance model that will allow developers and users to explore the performance potential of future heterogeneous systems for biological simulations. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we present an efficient, easily parallelizable approach to solve planted motif problem (PMP). PMP is a well-studied problem in computational biology. It is useful in developing methods for finding transcription factor binding sites, classifying sequences, and building phylogenetic trees. Many approaches to solve PMP can be found in the literature. But the problem with those approaches is that they are difficult to parallelize as they have been designed for serial computers. In this paper, we propose a simple, easily parallelizable enumeration-based approach called BitBased. As with most other enumeration-based approaches that have been proposed to solve PMP, BitBased is also limited by memory for solving large-sized problems. To overcome this limitation, we propose various modifications, which not only reduce the memory requirement but also improve the performance of the approach. We have implemented our approach on multicore and GPU devices. We found that BitBased outperforms all the approaches proposed to solve PMP so far. BitBased is able to solve the (21,8) instance, which was not previously reported as solved in the literature. Copyright © 2012 John Wiley & Sons, Ltd.

Multiagent learning provides a promising paradigm to study how autonomous agents learn to achieve coordinated behavior in multiagent systems. In multiagent learning, the concurrency of multiple distributed learning processes makes the environment nonstationary for each individual learner. Developing an efficient learning approach to coordinate agents’ behavior in this dynamic environment is a difficult problem especially when agents do not know the domain structure and at the same time have only local observability of the environment. In this paper, a coordinated learning approach is proposed to enable agents to learn where and how to coordinate their behavior in loosely coupled multiagent systems where the sparse interactions of agents constrain coordination to some specific parts of the environment. In the proposed approach, an agent first collects statistical information to detect those states where coordination is most necessary by considering not only the potential contributions from all the domain states but also the direct causes of the miscoordination in a conflicting state. The agent then learns to coordinate its behavior with others through its local observability of the environment according to different scenarios of state transitions. To handle the uncertainties caused by agents’ local observability, an optimistic estimation mechanism is introduced to guide the learning process of the agents. Empirical studies show that the proposed approach can achieve a better performance by improving the average agent reward compared with an uncoordinated learning approach and by reducing the computational complexity significantly compared with a centralized learning approach.Copyright © 2012 John Wiley & Sons, Ltd.

One of the main factors that affect the performance of the sparse matrix–vector product (SpMV) is the low data reuse caused by the irregular and indirect memory access patterns. Different strategies to deal with this problem such as data reordering techniques have been proposed. The computational cost of these techniques is typically high because they consider all the nonzeros of the sparse matrix in order to find an appropriate permutation of rows and columns that improves the SpMV performance. In this paper, we analyze the possibility of increasing the locality of the SpMV using incomplete information in the reordering process. This partial information comes as a consequence of considering only a subset of the nonzero elements of the matrix. These nonzeros are obtained from the original matrix through a sampling process. In particular, two different sampling methods have been considered: a random sampling and an event-based sampling using hardware counters. We have detected that a small number of samples is enough to obtain quality reorderings. As a consequence, using sampling-based reorderings leads to noticeable performance improvements with respect to the non-reordered matrices, reaching speedup values up to 2.1 × . In addition, an important reduction in the computational time required by the reordering technique has been observed. Copyright © 2012 John Wiley & Sons, Ltd.

This paper introduces SIMinG-1k—a manycore simulator infrastructure. SIMinG-1k is a graphics processing unit accelerated, parallel simulator for design-space exploration of large-scale manycore systems. It features an optimal trade-off between modeling accuracy and simulation speed. Its main objectives are high performance, flexibility, and ability to simulate thousands of cores. SIMinG-1k can model different architectures (currently, we support ARM (Available from: http://infocenter.arm.com/help/index.jsp?topic=/com.arm.doc.ddi0100i/index.html) and Intel x86) using two-step approac where architecture specific front end is decoupled from a fast and parallel manycore virtual machine running on graphical processing unit platform. We evaluate the simulator for target architecture with up to 4096 cores. Our results demonstrate very high scalability and almost linear speedup with simulation of increasing number of cores.Copyright © 2012 John Wiley & Sons, Ltd.

In the area of high-performance computing and embedded systems, numerous code optimisation methods exist to accelerate the speed of the computation (or optimise another performance criteria). They are usually experimented by doing multiple observations of the initial and the optimised execution times of a programme in order to declare a speedup. Even with fixed input and execution environment, programme execution times vary in general. Hence, different kinds of speedups may be reported: the speedup of the average execution time, the speedup of the minimal execution time, the speedup of the median and others. Many published speedups in the literature are observations of a set of experiments. To improve the reproducibility of the experimental results, this article presents a rigorous statistical methodology regarding programme performance analysis. We rely on well-known statistical tests (Shapiro–Wilk's test, Fisher's F-test, Student's t-test, Kolmogorov–Smirnov's test and Wilcoxon–Mann–Whitney's test) to study if the observed speedups are statistically significant or not. By fixing 0 < α < 1 a desired risk level, we are able to analyse the statistical significance of the average execution time as well as the median. We can also check if , the probability that an individual execution of the optimised code is faster than the individual execution of the initial code. In addition, we can compute the confidence interval of the probability to obtain a speedup on a randomly selected benchmark that does not belong to the initial set of tested benchmarks. Our methodology defines a consistent improvement compared with the usual performance analysis method in high-performance computing. We explain in each situation the hypothesis that must be checked to declare a correct risk level for the statistics. The Speedup-Test protocol certifying the observed speedups with rigorous statistics is implemented and distributed as an open source tool based on R software. Copyright © 2012 John Wiley & Sons, Ltd.

There is an increasing interest in the distribution of parallel random number streams in the high-performance computing community particularly, with the manycore shift. Even if we have at our disposal statistically sound random number generators according to the latest and thorough testing libraries, their parallelization can still be a delicate problem. Indeed, a set of recent publications shows it still has to be mastered by the scientific community. With the arrival of multi-core and manycore processor architectures on the scientist desktop, modelers who are non-specialists in parallelizing stochastic simulations need help and advice in distributing rigorously their experimental plans and replications according to the state of the art in pseudo-random numbers parallelization techniques. In this paper, we discuss the different partitioning techniques currently in use to provide independent streams with their corresponding software. In addition to the classical approaches in use to parallelize stochastic simulations on regular processors, this paper also presents recent advances in pseudo-random number generation for general-purpose graphical processing units. The state of the art given in this paper is written for simulation practitioners. Copyright © 2012 John Wiley & Sons, Ltd.

A new class of efficient and flexible hardware accelerators for DNA local sequence alignment based on the widely used Smith–Waterman algorithm is proposed in this paper. This new class of accelerating structures exploits an innovative technique that tracks the origin coordinates of the best alignment to allow a significant reduction of the size of the dynamic programming matrix that needs to be recomputed during the subsequent traceback phase, providing a considerable reduction of the resulting time and memory requirements. The significant performance of the enhanced class of accelerators is attained by also providing support for an additional level of parallelism: the capability to concurrently align several query sequences with one or more reference sequences, according to the specific application requisites. Moreover, the accelerator class also includes specially designed processing elements that improve the resource usage when implemented in a Field Programmable Gate Array (FPGA), and easily provide several different configurations in an Application Specific Integrated Circuit (ASIC) implementation. Obtained results demonstrated that speedups as high as 278 can be obtained in ASIC accelerating structures. A FPGA-based prototyping platform, operating at a 40 times lower clock frequency and incorporating a complete alignment embedded system, still provides significant speedups as high as 27, compared with a pure software implementation.Copyright © 2012 John Wiley & Sons, Ltd.

Cloud computing represents a novel on-demand computing technology where resources are provisioned in compliance to a set of predefined non-functional properties specified and negotiated by means of service level agreements (SLAs). Currently, cloud providers strive to achieve efficient SLA enforcement strategies to avoid costly SLA violations during application provisioning and to timely react to failures and environmental changes. These strategies include advanced application deployment mechanisms and appropriate resource monitoring concepts. In terms of cloud resource monitoring, providers tend to adopt existing monitoring tools, such as those from grid environments. However, those tools are usually restricted to locality and homogeneity of monitored objects, are not scalable, and do not support mapping of low-level resource metrics (e.g., system uptime and downtime) to high-level application-specific SLA parameters (e.g., system availability). In this paper, we present a novel low-level metrics to high-level SLA (LoM2HiS) framework for managing the monitoring of low-level resource metrics and mapping them to high-level SLAs and an application deployment mechanism for scheduling and provisioning applications in clouds. The LoM2HiS framework provides the application deployment mechanism with monitored information and SLA violation prevention techniques, thereby ensuring the performance of the applications and thus increasing the revenue of the cloud provider by avoiding SLA violation penalty cost. This framework is the building block of the Foundations of Self-governing ICT Infrastructures project, which intends to facilitate autonomic SLA management and enforcement. Thus, the LoM2HiS framework detects future SLA violation threats and can notify the knowledge component to act so as to avert the threats. We discuss in detail the conceptual design of the LoM2HiS framework and the application deployment mechanism including their implementations. Finally, we present our evaluation results based on a use-case scenario demonstrating the usage of the LoM2HiS framework in a real cloud environment. Copyright © 2012 John Wiley & Sons, Ltd.

In several scientific and business domains, very large data repositories are generated. To find interesting and useful information in those repositories, efficient data mining techniques and knowledge discovery processes must be used. The exploitation of data mining techniques in science helps scientists in hypothesis formation and gives them a support on their scientific practices, whereas in industrial processes, data mining can exploit existing data sources as a real value for companies that can take advantage from the knowledge that can be extracted from their large data sources. Data mining tasks are often composed by multiple stages that may be linked to each other to form various execution flows. Moreover, data mining tasks are often distributed because they involve data and tools located over geographically distributed environments. Therefore, it is fundamental to exploit effective paradigms, such as services and workflows, to model data mining tasks that are both multi-staged and distributed. This paper discusses data mining services and workflows for analyzing scientific data in high-performance distributed environments such as Grids and Clouds. We discuss how it is possible to define basic and complex services for supporting distributed data mining tasks in Grids. We also present a workflow formalism and a service-oriented programming framework, named DIS3GNO, for designing and running distributed knowledge discovery processes in the Knowledge Grid system. DIS3GNO supports all the phases of a knowledge discovery process, including composition, execution, and results visualization. After introducing DIS3GNO, some relevant use cases implemented by it and a performance evaluation of the system are discussed.Copyright © 2012 John Wiley & Sons, Ltd.

Developing high-quality, error-free message-passing concurrent programs is not trivial. Although a number of different primitives with associated semantics are available to assist such development, they often increase the complexity of the testing process. In this paper, we extend our previous test model for message-passing programs and present new structural testing criteria, taking into account additional features used in this paradigm, such as collective communication, non-blocking sends, distinct semantics for non-blocking receives, and persistent operations. Our new model also recognizes that sender primitives cannot always be matched with every receive primitive. This improvement allows us to remove statically a significant number of infeasible synchronization edges that would otherwise have to be analyzed later by the tester. In this paper, the test model is presented using the Message-Passing Interface standard; however, our new model has been designed to be flexible, and it can be configured to support a range of different message-passing environments or languages. We have carried out case studies showing the applicability of the new test model to represent message-passing programs and also to reveal errors, mainly those errors related to inter-process communication. In addition to increasing the number of features supported by the test model, we have also reduced the overall cost of testing significantly. Our case studies suggest that the number of synchronization edges can be reduced by up to 93%, mainly by eliminating infeasible edges between unmatchable communication primitives. The main contribution of the paper is to present a more flexible test model that provides improved coverage for message-passing programs and at the same time reduces the cost of testing significantly. Copyright © 2012 John Wiley & Sons, Ltd.

The continuing revolution in DNA sequencing and biological sensor technologies is driving a digital transformation to our approaches for observation, experimentation, and interpretation that form the foundation of modern biology and genomics. Whereas classical experiments were limited to thousands of hand-collected observations, today's improved sensors allow billions of digital observations and are improving at an exponential rate that exceeds Moore's law. These improvements have made it possible to monitor the dynamics of biological processes on an unprecedented scale, but have proportionally greater quantitative and computational requirements.

The exponentially growing digital demands have motivated extensive research into improved algorithms and parallel systems. Recently, a great deal of research has been focused on applying emerging scalable computing systems to genomic research. One of the most promising is the Hadoop open-source implementation of MapReduce: it is specifically designed to scale to very large datasets, its intuitive design supports rich parallel algorithms, and is naturally applied to analysis of many biological assays. There has also been success accelerating numerically intensive genomics applications using heterogeneous processors such as GPUs and FPGAs. These are promising early results, but it is clear that continued computational research will become even more important in the years to come. Copyright © 2012 John Wiley & Sons, Ltd.

Real-time and safety-critical code could benefit from the use of design patterns and frameworks that rely on subtyping and dynamic dispatch. However, modular reasoning about programs that use subtypes requires that each overriding method obeys the specifications of all methods that it overrides. For example, if method scale is specified in a supertype Vector2d to take at most 42 ns to execute, then an override of scale cannot take more than 42 ns to execute in any subtype, such as Vector3d. The problem is that subtype objects typically contain more information, such as the z coordinate in Vector3d, and thus their methods often require more time to execute than the methods they override. In this paper, we show how to specify timing constraints for subtypes in a way that both allows overriding subtype methods to have more time to execute and yet permits precise modular verification and checking of timing constraints. Our techniques allow object-oriented coding and design patterns based on subtype polymorphism to be used in real-time and safety-critical software. Copyright © 2012 John Wiley & Sons, Ltd.

The advent of new sequencing technologies has generated extremely large amounts of information. To successfully apply bioinformatics tools to such large datasets, they need to exhibit scalability and ideally elasticity in diverse computing environments. We describe the application of previously obtained lessons to a new workflow with and without shared file storage. Because the original workflows have an intractable sequential running times on large datasets, we propose lessons and results for refactoring bioinformatics tools for elastic scaling on personal clouds. Our case studies describe the various challenges faced when constructing such a workflow, from dealing with failure detection, to managing dependencies, to handling the quirks of the underlying operating systems. The practice of scaling bioinformatics tools is increasingly commonplace. As such, this hands-on application of refactoring techniques can serve as a valuable guide. Significantly, our customized Makeflow framework enabled generalizable deployment on a wider variety of systems while substantially reducing wall clock runtimes using hundreds of cores. Copyright © 2012 John Wiley & Sons, Ltd.

We report the results of a scaling effort that increases both the speed and resolution of the SIESTA magnetohydrodynamics equilibrium code. SIESTA is capable of computing three-dimensional plasma equilibria with magnetic islands at high spatial resolutions for toroidally confined plasmas. Starting with a small-scale parallel implementation, we identified scale-dependent bottlenecks of the code and developed scalable alternatives for each performance-significant functionality, cumulatively improving both its runtime speed (on the same number of processors) and its scalability (across larger number of processors) by an order of magnitude. The net outcome is an improvement in speed by over 10-fold, utilizing a few thousand processors, enabling SIESTA to simulate high spatial-resolution scenarios in under an hour for the first time. Copyright © 2012 John Wiley & Sons, Ltd.

The MapReduce programming model has proven useful for data-driven high throughput applications. However, the conventional MapReduce model limits itself to scheduling jobs within a single cluster. As job sizes become larger, single-cluster solutions grow increasingly inadequate. We present a hierarchical MapReduce framework that utilizes computation resources from multiple clusters simultaneously to run MapReduce job across them. The applications implemented in this framework adopt the Map–Reduce–GlobalReduce model where computations are expressed as three functions: Map, Reduce, and GlobalReduce. Two scheduling algorithms are proposed, one that targets compute-intensive jobs and another data-intensive jobs, evaluated using a life science application, AutoDock, and a simple Grep. Data management is explored through analysis of the Gfarm file system.Copyright © 2012 John Wiley & Sons, Ltd.

Multicore computers are ubiquitous. Expert developers as well as developers with little experience in parallelism are now asked to create multithreaded software to exploit parallelism in mainstream shared-memory hardware. However, finding and fixing parallel programming errors is a complex and arduous task. Programmers thus rely on tools such as race detectors that typically focus on reporting errors due to incorrect usage of synchronization constructs or due to missing synchronization. This arsenal of debugging techniques, however, is incomplete. This article presents a new perspective and addresses a largely unexplored direction of defect localization where a wrong usage of nonparallel programming constructs might cause wrong parallel application behavior. In particular, we make a contribution by showing how to use data-mining techniques to locate defects in multithreaded shared-memory programs. Our technique analyzes execution anomalies in a condensed representation of the dynamic call graphs of a multithreaded object-oriented application and identifies methods that contain a defect. Compared with race detectors that concentrate on finding incorrect synchronization, our method is able to reveal a wider range of defects that affect the control flow of a parallel program. Results from controlled experiments show that our data-mining approach finds not only race conditions in different types of multicore applications but also other errors that cause incorrect parallel program behavior. Data-mining techniques offer a fruitful new ground for parallel program debugging, and we also discuss long-term directions for this interesting field. Copyright © 2012 John Wiley & Sons, Ltd.

As the recent proliferation of social networks, mobile applications, and online services increased the rate of data gathering, to find near-duplicate records efficiently has become a challenging issue. Related works on this problem mainly aim to propose efficient approaches on a single machine. However, when processing large-scale dataset, the performance to identify duplicates is still far from satisfactory. In this paper, we try to handle the problem of duplicate detection applying MapReduce. We argue that the performance of utilizing MapReduce to detect duplicates mainly depends on the number of candidate record pairs and intermediate result size, which is related to the shuffle cost among different nodes in cluster. In this paper, we proposed a new signature scheme with new pruning strategies to minimize the number of candidate pairs and intermediate result size. The proposed solution is an exact one, which assures none duplicate record pair can be lost. The experimental results over both real and synthetic datasets demonstrate that our proposed signature-based method is efficient and scalable.Copyright © 2012 John Wiley & Sons, Ltd.

Garbage collection is a well-known technique to increase program safety and developer productivity. Within the past few years, it has also become feasible for uniprocessor hard real-time systems. However, garbage collection for multi-processors does not yet meet the requirements of hard real-time systems. In this paper, we present a hard real-time garbage collector for a Java chip multi-processor that provides non-disruptive and analyzable behavior. For retrieving the references in local variables of threads, we propose a protocol that minimizes disruptions for high-priority tasks while still providing good bounds on the time until stack scanning finishes. Also, we developed a hardware unit that enables transparent, preemptible copying of objects, which eliminates the need to block tasks while copying objects. Evaluation of the hardware shows that the copy unit introduces only little overhead and does not limit the critical path. Analyses for different aspects of the system are presented, which indicate that comprehensive analysis of the presented system is indeed possible. Measurements resulted in release jitter for high-priority tasks of 362 μs or less on an embedded multi-processor with eight cores clocked at 100 MHz. This indicates that with the proposed garbage collector, high scheduling quality and garbage collection do not contradict each other on chip multi-processors. Copyright © 2012 John Wiley & Sons, Ltd.

Virtual molecular docking is a computational method used in computer-aided drug discovery that calculates the binding affinity of a small molecule drug candidate to a target protein. High-throughput virtual screenings calculate the binding affinities for a large number of molecules at once and ranks potential drug candidates to greatly reduces the time and cost of suggesting new potential pharmaceuticals. This high-throughput screening is a task parallel process and therefore well-suited for distributed computing. In this study, we use the open source Hadoop framework implementing the MapReduce paradigm for distributed computing on a cloud platform and the widely used molecular docking program, AutoDock. The initial implementation of AutoDockCloud showed a speed-up of 450 on Kandinsky, a cloud computer located at Oak Ridge National Laboratory. Further modifications show promise for a greater speed-up of large chemical library screenings and also incorporates and distributes the pre-docking procedures. Copyright © 2012 John Wiley & Sons, Ltd.

The statistical language R is favoured by many biostatisticians for processing microarray data. In recent times, the quantity of data that can be obtained in experiments has risen significantly, making previously fast analyses time consuming or even not possible at all with the existing software infrastructure. High performance computing (HPC) systems offer a solution to these problems but at the expense of increased complexity for the end user. The Simple Parallel R Interface is a library for R that aims to reduce the complexity of using HPC systems by providing biostatisticians with drop-in parallelised replacements of existing R functions. In this paper we describe parallel implementations of two popular techniques: exploratory clustering analyses using the random forest classifier and feature selection through identification of differentially expressed genes using the rank product method. Copyright © 2012 John Wiley & Sons, Ltd.

Energy consumption has become a major design constraint in modern computing systems. With the advent of petaflops architectures, power-efficient software stacks have become imperative for scalability. Techniques such as dynamic voltage and frequency scaling (called DVFS) and CPU clock modulation (called throttling) are often used to reduce the power consumption of the compute nodes. To avoid significant performance losses, these techniques should be used judiciously during parallel application execution. For example, its communication phases may be good candidates to apply the DVFS and CPU throttling without incurring a considerable performance loss. They are often considered as indivisible operations although little attention is being devoted to the energy saving potential of their algorithmic steps. In this work, two important collective communication operations, all-to-all and allgather, are investigated as to their augmentation with energy saving strategies on the per-call basis. The experiments prove the viability of such a fine-grain approach. They also validate a theoretical power consumption estimate for multicore nodes proposed here. While keeping the performance loss low, the obtained energy savings were always significantly higher than those achieved when DVFS or throttling were switched on across the entire application run.Copyright © 2012 John Wiley & Sons, Ltd.

It is generally accepted that the ability to develop large-scale distributed applications has lagged seriously behind other developments in cyberinfrastructure. In this paper, we provide insight into how such applications have been developed and an understanding of why developing applications for distributed infrastructure is hard. Our approach is unique in the sense that it is centered around half a dozen existing scientific applications; we posit that these scientific applications are representative of the characteristics, requirements, as well as the challenges of the bulk of current distributed applications on production cyberinfrastructure (such as the US TeraGrid). We provide a novel and comprehensive analysis of such distributed scientific applications. Specifically, we survey existing models and methods for large-scale distributed applications and identify commonalities, recurring structures, patterns and abstractions. We find that there are many ad hoc solutions employed to develop and execute distributed applications, which result in a lack of generality and the inability of distributed applications to be extensible and independent of infrastructure details. In our analysis, we introduce the notion of application vectors: a novel way of understanding the structure of distributed applications. Important contributions of this paper include identifying patterns that are derived from a wide range of real distributed applications, as well as an integrated approach to analyzing applications, programming systems and patterns, resulting in the ability to provide a critical assessment of the current practice of developing, deploying and executing distributed applications. Gaps and omissions in the state of the art are identified, and directions for future research are outlined. Copyright © 2012 John Wiley & Sons, Ltd.

Several ab initio computational methods for protein structure prediction have been designed using full-atom models and force field potentials to describe interactions among atoms. Those methods involve the solution of a combinatorial problem with a huge search space. Genetic algorithms (GAs) have shown significant performance increases for such methods. However, even a small protein may require hundreds of thousands of energy function evaluations making GAs suitable only for the prediction of very small proteins. We propose an efficient technique to compute the van der Waals energy (the greatest contributor to protein stability) speeding up the whole GA. First, we developed a Cell-List Reconstruction procedure that divides the tridimensional space into a cell grid for each new structure that the GA generates. The cells restrict the calculations of van der Waals potentials to ranges in which they are significant, reducing the complexity of such calculations from quadratic to linear. Moreover, the proposal also uses the structure of the cell grid to parallelize the computation of the van der Waals energy, achieving additional speedup. The results have shown a significant reduction in the run time required by a GA. For example, the run time for the prediction of a protein with 147,980 atoms can be reduced from 217 days to 7 h. Copyright © 2012 John Wiley & Sons, Ltd.

We explore the comparative performance of the Cray XMT and XMT-2 massively multithreaded supercomputers. We use benchmarks to evaluate memory accesses for various types of loops. We also compare the performance of these machines on matrix multiply and on three previously implemented dynamic programming algorithms. It is shown that the relative performance of these machines is dependent on the size (number of processors) of the configuration, as well as the size of the problem being evaluated. In particular, small configurations of the original XMT can sometimes show slightly better performance than larger configurations of the XMT-2, for the same problem size. We note that, under heavy memory load, performance of loops can saturate well before the maximum number of processors available. This suggests that it may not always be useful to use the maximum number of processors for a specific run. We also show that manual restructuring of nested loops, including decreasing the parallelism, can result in major improvements in performance. The results in this paper indicate that careful exploration of the space of problem sizes, number of processors used, and choices of loop parallelization can yield substantial improvements in performance. These improvements can be very significant for production codes that run for extended periods of time.Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we study CPU utilization time patterns of several MapReduce applications. After extracting running patterns of several applications, the patterns along with their statistical information are saved in a reference database to be later used to tweak system parameters to efficiently execute future unknown applications. To achieve this goal, CPU utilization patterns of new applications along with its statistical information are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different pattern lengths, dynamic time warping (DTW) is utilized for such comparison; a statistical analysis is then applied to DTWs' outcomes to select the most suitable candidates. Furthermore, under a hypothesis, we also proposed another algorithm to classify applications under similar CPU utilization patterns. Finally, dependency between minimum distance/maximum similarity of applications and scalability (in both input size and number of virtual nodes) is studied. Here, we used widely used applications (WordCount, Distributed Grep, and Terasort) as well as an Exim MainLog parsing application to evaluate our hypothesis in automatic tweaking MapReduce configuration parameters in executing similar applications scalable on both size of input data and number of virtual nodes. Results are very promising and showed the effectiveness of our approach on a private cloud with up to 25 virtual nodes. Concurrency and Computation: Practice and Experience, 2012. Copyright © 2012 John Wiley & Sons, Ltd.

Cloud computing is attracting the attention of the scientific community. In this paper, we develop a new cloud-based computing system in the Windows Azure platform that allows users to use the Zeolite Structure Predictor (ZSP) model through a Web browser. The ZSP is a novel machine learning approach for classifying zeolite crystals according to their framework type. The ZSP can categorize entries from the Inorganic Crystal Structure Database into 41 framework types. The novel automated system permits a user to calculate the vector of descriptors used by ZSP and to apply the model using the Random Forest™ algorithm for classifying the input zeolite entries. The workflow presented here integrates executables in Fortran and Python for number crunching with packages such as Weka for data analytics and Jmol for Web-based atomistic visualization in an interactive compute system accessed through the Web. The compute system is robust and easy to use. Communities of scientists, engineers, and students knowledgeable in Windows-based computing should find this new workflow attractive and easy to be implemented in scientific scenarios in which the developer needs to combine heterogeneous components.Copyright © 2012 John Wiley & Sons, Ltd.

As a large number of corpuses are represented, stored and published in XML format, how to find useful information from XML databases has become an increasingly important issue. Keyword search enables web users to easily access XML data without the need to learn a structured query language or to study complex data schemas. Most existing indexing strategies for XML keyword search are based upon Dewey encoding. In this paper, we proposed a new encoding method called Level Order and Father (LAF) for XML documents. With LAF encoding, we devised a new index structure, called two-layer LAF inverted index, which can greatly decrease the space complexity compared with Dewey encoding-based inverted index. Furthermore, with two-layer LAF inverted index, we proposed a new keyword query algorithm called Algorithm based on Binary Search (ABS) that can quickly find all Smallest Lowest Common Ancestor. We experimentally evaluate two-layer LAF inverted index and ABS algorithm on four real XML data sets selected from Wikipedia. The experimental results prove the advantages of our index method and querying algorithm. The space consumed by two-layer LAF index is less than half of that consumed by Dewey inverted index. Moreover, ABS is about one to two orders of magnitude faster than the classic Stack algorithm. Concurrency and Computation: Practice and Experience, 2012.© 2012 Wiley Periodicals, Inc.

Automatic service collaboration calls for the development of semantically structured service resource space to maximize the utility of Web services. Semantic links contain rich semantic information that may indicate important relationships among services. We provided an effective method for constructing multi-dimensional service resource space based on semantic links for service collaboration, in which similar and related semantic relationship between services are considered. We first clustered services with similar and related relations on the basis of a hierarchical structure respectively and then took advantage of the resource space model to construct multi-dimensional service resource space. Finally, experimental results show the effectiveness of the method. Concurrency and Computation: Practice and Experience, 2012.© 2012 Wiley Periodicals, Inc.

In futures markets with price limits, trading halts are triggered by limit hits. Limit hits are rarely observed, perhaps because traders avoid bid-ask quotes that cause them. If this explanation is true, futures prices would cluster in a narrow region close to the limits. We test this empirically for currency futures contracts and find results consistent with the explanation. The tests require calculations of all combinations of a computationally intensive time series, which are extremely time consuming on a sequential machine and hence limit the practicality of the analysis. Consequently, we investigate parallel computing strategies in partitioning the datasets and solving them in parallel on a high-end Beowulf cluster. We discuss two different partitioning strategies of the given datasets on the cluster and elaborate the results. Copyright © 2012 John Wiley & Sons, Ltd.

A major design issue in embedded systems is reducing the power consumption because batteries have a limited energy budget. For this purpose, several techniques such as dynamic voltage and frequency scaling (DVFS) or task migration are being used. DVFS allows reducing power by selecting the optimal voltage supply, whereas task migration achieves this effect by balancing the workload among cores. This paper focuses on power-aware scheduling allowing task migration to reduce energy consumption in multicore embedded systems implementing DVFS capabilities. To address energy savings, the devised schedulers follow two main rules: migrations are allowed at specific points of time and only one task is allowed to migrate each time. Two algorithms have been proposed working under real-time constraints. The simpler algorithm, namely, single option migration (SOM) only checks just one target core before performing a migration. In contrast, the multiple option migration (MOM) searches the optimal target core. In general, the MOM algorithm achieves better energy savings than the SOM algorithm, although differences are wider for a reduced number of cores and frequency/voltage levels. Moreover, the MOM algorithm reduces energy consumption as much as 40% over the worst fit algorithm. Copyright © 2012 John Wiley & Sons, Ltd.

Key-value stores are widely used on large-scale data management in the cloud environment. However, they can only naturally support key-based queries, and do not have efficient solutions for value-based queries. Thus, dealing with high-dimensional data in key-value stores is still a big challenge. State-of-the-art solutions apply value-based tree-structure indexes to solve this issue. These methods suffer from the curse of dimensionality and cannot achieve satisfactory performance. They also bring serious load unbalancing problem among servers, and result in dramatic system scalability degradation.

Meanwhile, similarity search in high-dimensional data space becomes more and more popular in today's cloud applications. Due to the lack of efficient algorithms for value-based queries, users have to wait for a long time before the results are returned. To address this issue, we propose a novel approach called high-dimensional similarity query in key-value stores (HDKV), which can generate similarity results in a short time and maintain good database scalability. In HDKV, a strict order-preserving hash function is designed to map nearby objects in the high-dimensional space onto adjacent keys of a continuous linear space in key-value stores. With this strategy, many expensive random accesses are replaced with more efficient scan accesses. The experimental evaluation on real world data set shows that compared to the state-of-the-art methods, HDKV can dramatically reduce the search time with little impact on the accuracy. Copyright © 2012 John Wiley & Sons, Ltd.

Delay tolerant networks (DTNs) are resource-constrained dynamic networks where a continuous end-to-end connectivity is not always available. In such a challenging network, a fixed infrastructure may not be connected when a DTN is partitioned or the message delay in the network is large. Thus, the traditional public key infrastructure system and identity-based encryption (IBE) system are not suitable for DTNs because they rely on centralized infrastructures and require multiple round-trip interactions. To address this issue, we propose a distributed secret key generation system with self-certified identity (SCI-DKG) that does not require any private key generator and threshold cryptosystem. Initially, each node generates a private key and distributes an initial message including a self-certified identity and secret sharings to members in a DTN. Receivers independently authenticate the identity and extracts some encryption parameters corresponding to the identity from this initial message. We prove that SCI-DKG is chosen ciphertext secure in the standard model, and it can resist potential network attacks. Simulation results show that SCI-DKG has smaller delay and higher successful ratio of secret key generation compared with IBE and hierarchical IBE systems implemented in a DTN. Copyright © 2012 John Wiley & Sons, Ltd.

The globally integrated contemporary business environment has prompted new challenges to database architectures in order to enable organizations to improve database applications performance, scalability, reliability and data privacy in adapting to the evolving nature of business. Although a number of distributed database architectures are available for choice, there is a lack of an in-depth understanding of the performance characteristics of these database architectures in a comparison way. In this paper, we report a performance study of three typical (centralized, partitioned and replicated) database architectures. We used the TPC-C as the evaluation benchmark to simulate a contemporary business environment, and a commercially available database management system that supports the three architectures. We compared the performance of the partitioned and replicated architectures against the centralized database, which results in some interesting observations and practical experience. The findings and the practice presented in this paper provide useful information and experience for the enterprise architects and database administrators in determining the appropriate database architecture in moving from centralized to distributed environments. Copyright © 2012 John Wiley & Sons, Ltd.

Beyond the traditional BitTorrent, a new genre of peer-to-peer communications protocol for worldwide file sharing is rapidly evolving towards private BitTorrent (PT). In recent years, a proliferation of PT communities have emerged. To enhance the user experience, account-based share-ratio enforcement (SRE) has been developed and widely adopted. Whereas existing studies mainly take SRE as an incentive, we discover that it also plays a critical role in selecting and filtering users. In addition to SRE, a rich set of user management rules, such as registration management, banning policies, and user caste system, are also studied in this paper. This includes to explore their effects on user behavior, download performance, content availability, and system scalability. The measurement results presented in this paper are based on large-scale experiments conducted over six representative PT sites for over a year. We find that the stricter registration will lead to fewer new users, resulting in a scalability problem, which is critical for the PT communities because the download performance and content availability depend on not only the contribution of users but also the population of the community. Our measurement and analysis pose a direction for the design of new incentive mechanisms that take the difficulty of enrollment into the consideration.Copyright © 2012 John Wiley & Sons, Ltd.

We consider a task graph to be executed on a set of processors. We assume that the mapping is given, say by an ordered list of tasks to execute on each processor, and we aim at optimizing the energy consumption while enforcing a prescribed bound on the execution time. Although it is not possible to change the allocation of a task, it is possible to change its speed. Rather than using a local approach such as backfilling, we consider the problem as a whole and study the impact of several speed variation models on its complexity. For continuous speeds, we give a closed-form formula for trees and series–parallel graphs, and we cast the problem into a geometric programming problem for general directed acyclic graphs. We show that the classical dynamic voltage and frequency scaling (DVFS) model with discrete modes leads to an NP-complete problem, even if the modes are regularly distributed (an important particular case in practice, which we analyze as the incremental model). On the contrary, the Vdd-hopping model that allows to switch between different supply voltages (V_DD) while executing a task leads to a polynomial solution. Finally, we provide an approximation algorithm for the incremental model, which we extend for the general DVFS model. Copyright © 2012 John Wiley & Sons, Ltd.

There have been few efforts to date to write physics algorithms for general unstructured meshes (meshes composed of arbitrary polygons/polyhedra) on graphics processing units (GPUs). Typical strategies for GPU memory management, such as double-buffering and coalescing memory accesses, are difficult to apply to the irregular memory storage patterns of unstructured meshes. This paper presents results from an initial GPU version of a typical unstructured mesh kernel. Three different memory management strategies are described and implemented. Timing results for all three strategies are presented, in some cases showing speedups of over 20 times compared with the original CPU code.Copyright © 2012 John Wiley & Sons, Ltd.

The curse of dimensionality is a ubiquitous problem for multiagent reinforcement learning, which means the learning and storing space grows exponentially with the number of agents and hinders the application of multiagent reinforcement learning. To relieve this problem, we propose a new framework named as optimal tracking agent (OTA). The OTA views the other agents as part of the environment and uses a reduced form to learn the optimal decision. Although merging other agents into the environment may reduce the dimension of action space, the environment characterized by such form is dynamic and does not satisfy the convergence of reinforcement learning (RL). Thus, we develop an estimator to track the dynamics of the environment. The estimator obtains the dynamic model, and then the model-based RL can be used to react to the dynamic environment optimally. Because the Q-function in OTA is also a dynamic process because of other agents’ dynamics, different from traditional RL, in which the learning is a stationary process and the usual action selection mechanisms just suit to such stationary process, we improve the greedy action selection mechanism to adapt to such dynamics. Thus, the OTA will have convergence. An experiment illustrates the validity and efficiency of the OTA.Copyright © 2012 John Wiley & Sons, Ltd.

One of the principal goals of cloud computing is the outsourcing of the hosting of data and applications, thus enabling a per-usage model of computation. Data and applications may be packaged in virtual machines (VM), which are themselves hosted by nodes, that is, physical machines. Several frameworks have been designed to manage VMs on pools of physical machines; most of them, however, do not efficiently address a major objective of cloud providers: maximizing system utilization while ensuring the QoS. Several approaches promote virtualization capabilities to improve this trade-off. However, the dynamic scheduling of a large number of VMs as part of a large distributed infrastructure is subject to important and hard scalability problems that become even worse when VM image transfers have to be managed. Consequently, most current frameworks schedule VMs statically using a centralized control strategy. In this article, we present distributed VM scheduler, a framework that enables VMs to be scheduled cooperatively and dynamically in large-scale distributed systems. We describe, in particular, how several VM reconfigurations can be dynamically calculated in parallel and applied simultaneously. Reconfigurations are enabled by partitioning the system (i.e., nodes and VMs) on the fly. Partitions are created with a minimum of resources necessary to find a solution to the reconfiguration problem. Moreover, we propose an algorithm to handle deadlocks that may appear because of the partitioning policy. We have evaluated our prototype through simulations and compared our approach with a centralized one. The results show that our scheduler permits VMs to be reconfigured more efficiently: the time needed to manage thousands of VMs on hundreds of machines is typically reduced to a tenth or less. Copyright © 2012 John Wiley & Sons, Ltd.

Financial institutions have massive computations to carry out overnight, which are very CPU demanding. The challenge is to price many different products on a cluster-like architecture. We have used the Premia software to valuate the financial derivatives. In this work, we explain how Premia can be embedded into Nsp, a scientific software like MATLAB, to provide a powerful tool to valuate a whole portfolio. Finally, we have integrated an Message Passing Interface (MPI) toolbox into Nsp to enable the use of Premia to solve a bunch of pricing problems on a cluster. This unified framework can then be used to test different parallel architectures. Copyright © 2012 John Wiley & Sons, Ltd.

With the development of wireless mesh networks (WMNs), fault diagnosis in WMNs is becoming a very challenging task. In this paper, a two-level scheme for fault diagnosis in WMNs is presented. We partition the network into a two-level topology architecture where level 1 is composed of mesh clients and level 2 consists of mesh routers. A new comparison approach is introduced to diagnose the two levels. On the basis of the new comparison approach, every node in WMNs can be diagnosed either as fault-free or faulty. Our protocol assumes that the WMN's topology may change during the testing phase and utilize the shortest path spanning tree, which is constructed along with the process of fault diagnosis, to disseminate local messages and global messages in WMNs. The proposed model is only for static fault circumstances. We provide the analysis of correctness, communication complexity, and time complexity of our protocol, and the comparison between our protocol and others through both theoretical proof and practical simulation. The analysis shows that our model has significant advantages over other existing models. Copyright © 2012 John Wiley & Sons, Ltd.

Lock-based resource sharing protocols for single processor systems are well understood and supported in programming languages such as Ada and the Real-Time Specification for Java, and in Real-Time Operating Systems, such as those that conform to the Real-Time POSIX standard. In contrast, multiprocessor resource sharing protocols are still in their infancy with no agreed best practices, and yet current real-time programming languages and operating systems claim to be suitable for multiprocessor applications. This paper reviews the currently available multiprocessor resource allocation policies and analyzes their applicability to the main industry standard real-time programming languages. It then proposes a framework that allows programmers to define and implement their own locking policy. A prototype implementation of the framework for Ada is presented and evaluated.Copyright © 2012 John Wiley & Sons, Ltd.

For decades, biologists around the world have recorded animal sounds. As the number of records grows, so does the difficulty to manage them, presenting challenges to save, retrieve, share, and manage sounds. These challenges are complicated by the fact that animal sound recordings have specific peculiarities, associated to the context in which the sound was recorded. For example, sounds emitted by individuals that are in groups may be different from ones emitted by isolated individuals. Although these characteristics may be relevant to biologists, they are seldom explicit in the recording metadata. This paper discusses our ongoing research on the management of sound recordings, considering factors such as environmental or social contexts, which are not treated by current systems. This work exploits retrieval based on context analysis. Query parameters include context variables that are dynamically derived using public services and ontologies associated with sound recording metadata. Part of the results have been validated through a web prototype, discussed in the text.Copyright © 2012 John Wiley & Sons, Ltd.

Public Infrastructure as a Service (IaaS) clouds such as Amazon, GoGrid and Rackspace deliver computational resources by means of virtualisation technologies. These technologies allow multiple independent virtual machines to reside in apparent isolation on the same physical host. Dynamically scaling applications running on IaaS clouds can lead to varied and unpredictable results because of the performance interference effects associated with co-located virtual machines. Determining appropriate scaling policies in a dynamic non-stationary environment is non-trivial. One principle advantage exhibited by IaaS clouds over their traditional hosting counterparts is the ability to scale resources on-demand. However, a problem arises concerning resource allocation as to which resources should be added and removed when the underlying performance of the resource is in a constant state of flux. Decision theoretic frameworks such as Markov Decision Processes are particularly suited to decision making under uncertainty. By applying a temporal difference, reinforcement learning algorithm known as Q-learning, optimal scaling policies can be determined. Additionally, reinforcement learning techniques typically suffer from curse of dimensionality problems, where the state space grows exponentially with each additional state variable. To address this challenge, we also present a novel parallel Q-learning approach aimed at reducing the time taken to determine optimal policies whilst learning online. Copyright © 2012 John Wiley & Sons, Ltd.

Web services transactions are used to build efficient and reliable Web applications that are distributed across the Internet and are accessed by multiple simultaneous users. Current research develops various models and protocols to improve the performance and reliability of Web services transactions. However, there is little research on testing the different models and protocols of Web services transactions. This paper presents an abstract transaction model that patterns different Web services transactions standards. This model is capable of deriving concrete models to automatically generate test cases for different Web services transactions standards. The proposed model is implemented as a prototype system and is evaluated using a case of the Jboss Transaction. The evaluation shows that the proposed system has the capability to automatically generate test cases and detect possible failures of transactions running under different Web services transactions standards. Copyright © 2012 John Wiley & Sons, Ltd.

This paper is about using the existing Monte Carlo approach for pricing European and American contracts on a state-of-the-art graphics processing unit (GPU) architecture. First, we adapt on a cluster of GPUs two different suitable paradigms of parallelizing random number generators, which were developed for CPU clusters. Because in financial applications, we request results within seconds of simulation, the sufficiently large computations should be implemented on a cluster of machines. Thus, we make the European contract comparison between CPUs and GPUs using from one up to 16 nodes of a CPU/GPU cluster. We show that using GPUs for European contracts reduces the execution time by ∼ 40 and diminishes the energy consumed by ∼ 50 during the simulation. In the second set of experiments, we investigate the benefits of using GPUs’ parallelization for pricing American options that require solving an optimal stopping problem and which we implement using the Longstaff and Schwartz regression method. The speedup result obtained for American options varies between two and 10 according to the number of generated paths, the dimensions, and the time discretization.Copyright © 2012 John Wiley & Sons, Ltd.

Ontology as a formal representation of a domain knowledge has played an important role in a distributed environment whereby semantic interoperability is a major factor. In this paper, we particularly focus on a distributed ontology framework that utilizes Semantic Grid resources. The semantic representation in a machine-understandable format (i.e., an ontology) is the backbone that enables interoperability between different user nodes in a semantic grid environment. However, the domain knowledge represented within an ontology is not static. From time to time, its concepts, properties and relationships need to be replaced or updated. Although many existing work have been focusing on how to utilize an ontology to support interoperability within a distributed environment, they often assume a rather static ontology. This paper focuses on formalizing and validating the process of ontology update, whereby sections of one ontology O₂ are replaced by a subset extracted from another ontology O₁. In the first phase, a subset S₁ is extracted from ontology O₁. Then in the second phase, the concepts in ontology O₂ are replaced by S₁. At the end of the process, the resulting ontology O₂ must still be a valid ontology. A semantic completeness checking also needs to be conducted so that the updated ontology O₂ is complete. A case study based on the Unified Medical Language System ontology from the medical informatics domain is presented. We use a semantic grid environment to build a framework for reusing, extracting and updating an ontology using a SOA. These allow the subset extracted from one ontology, to replace sections of another ontology, using shared resources in the semantic grid environment. A prototype of the framework is built using Web Services and a complexity evaluation measure is presented. The results of several simulations show ontology update in the semantic grid is a viable solution and can be further optimized. Copyright © 2012 John Wiley & Sons, Ltd.

Cloud computing is offering new approaches for High Performance Computing (HPC) as it provides dynamically scalable resources as a service over the Internet. In addition, General-Purpose computation on Graphical Processing Units (GPGPU) has gained much attention from scientific computing in multiple domains, thus becoming an important programming model in HPC. Compute Unified Device Architecture (CUDA) has been established as a popular programming model for GPGPUs, removing the need for using the graphics APIs for computing applications. Open Computing Language (OpenCL) is an emerging alternative not only for GPGPU but also for any parallel architecture. GPU clusters, usually programmed with a hybrid parallel paradigm mixing Message Passing Interface (MPI) with CUDA/OpenCL, are currently gaining high popularity. Therefore, cloud providers are deploying clusters with multiple GPUs per node and high-speed network interconnects in order to make them a feasible option for HPC as a Service (HPCaaS). This paper evaluates GPGPU for high performance cloud computing on a public cloud computing infrastructure, Amazon EC2 Cluster GPU Instances (CGI), equipped with NVIDIA Tesla GPUs and a 10 Gigabit Ethernet network. The analysis of the results, obtained using up to 64 GPUs and 256-processor cores, has shown that GPGPU is a viable option for high performance cloud computing despite the significant impact that virtualized environments still have on network overhead, which still hampers the adoption of GPGPU communication-intensive applications. Copyright © 2012 John Wiley & Sons, Ltd.

Multicore network processors have been playing an increasingly important role in computational processes, which emphasize on scalability and parallelism of the systems, in distributed environments especially in Internet-based delay-sensitive applications. It is an important but unsolved issue, however, to efficiently schedule tasks in network processors with multicore and multithread for improving the system throughput as much as possible. Profiling can gather runtime environment information and guide the compiler to optimize programs through scheduling tasks based on the runtime context. This paper proposes a profiling-based task scheduling approach, targeting on improving the throughput of multicore network processor (Intel IXP) systems in the balanced pipeline way. In this work, we investigate a profiling-based task scheduling framework, a task scheduling algorithm, and a set of performance models. Our task allocation scheme maps tasks onto the pipeline architecture and multiple threads of network processors in parallel, which incorporates the profiling context and global thread refinement. We evaluate our task scheduling algorithm by implementing representative network applications on the Intel IXP network processor. Experimental results demonstrate that our algorithm is able to schedule tasks in a balanced pipeline fashion and achieve the high throughput and data transmission rate. Copyright © 2012 John Wiley & Sons, Ltd.

We present a graphics processing unit (GPU) parallelization of the computation of the price of exotic cross-currency interest rate derivatives via a partial differential equation (PDE) approach. In particular, we focus on the GPU-based parallel pricing of long-dated foreign exchange (FX) interest rate hybrids, namely power reverse dual currency (PRDC) swaps with Bermudan cancelable features. We consider a three-factor pricing model with FX volatility skew, which results in a time-dependent parabolic PDE in three spatial dimensions. Finite difference methods on uniform grids are used for the spatial discretization of the PDE, and the alternating direction implicit (ADI) technique is employed for the time discretization. We then exploit the parallel architectural features of GPUs together with the Compute Unified Device Architecture framework to design and implement an efficient parallel algorithm for pricing PRDC swaps. Over each period of the tenor structure, we divide the pricing of a Bermudan cancelable PRDC swap into two independent pricing subproblems, each of which can efficiently be solved on a GPU via a parallelization of the ADI timestepping technique. Numerical results indicate that GPUs can provide significant increase in performance over CPUs when pricing PRDC swaps. An analysis of the impact of the FX skew on such derivatives is provided. Copyright © 2012 John Wiley & Sons, Ltd.

In today's highly parametrized distributed computational environments, such as green grid clusters and clouds, the growing power and cooling rates are becoming the dominant part of the users' and system managers' budgets. Computational grids, owing to their sheer sizes, still require advanced methodologies and strategies for supporting the scheduling of the users' tasks and applications to the distributed resources. The efficient resource allocation becomes even more challenging when energy utilization, beyond the conventional scheduling criteria, such as Makespan, is treated as first-class additional scheduling objective. In this paper, we address the independent batch scheduling in computational grid as a bi-objective global minimization problem with Makespan and energy consumption as the main criteria. We apply the dynamic voltage and frequency scaling model for the management of the cumulative power energy utilized by the grid resources. We develop three genetic algorithms as energy-aware grid schedulers, which were empirically evaluated in three grid size scenarios in static and dynamic modes. The simulation results confirmed the effectiveness of the proposed genetic algorithm-based schedulers in the reduction of the energy consumed by the whole system and in dynamic load balancing of the resources in grid clusters, which is sufficient to maintain the desired quality level(s). Copyright © 2012 John Wiley & Sons, Ltd.

Peer-to-peer (P2P) computing systems have become very popular during the last years because of their ability to scale to a large number of users and efficient communication among peers. They can support complex computational processes, beyond simple file sharing, while offering advantages of decentralized distributed systems. However, such systems may suffer from availability and reliability. To increase availability and reliability, and therefore, improve the perception of peers, yielding to fast response times and rich experience, data replication techniques are the foremost means in such systems. Indeed, in many P2P applications, for example, in a groupware, documents generated along application life cycle can change over time. The need is then to efficiently replicate dynamic documents and data to support group processes and collaboration. In this paper, we propose a replication system for documents structured as XML files and evaluate it under different scenarios. The proposed system has a super-peer architecture that provides fast consistency for late joining peers. It uses optimistic replication techniques with propagating update operations from source to destination node in push mode. The system is suitable for asynchronous collaboration in online collaborative teams accomplishing a common project in a P2P environment. Copyright © 2012 John Wiley & Sons, Ltd.

The service-oriented distributed network requires more stable and persistent services, but the services from dishonest or unstable nodes would damage the correctness and availability of services. To efficiently obtain the services from high dependable nodes, many trust management systems have been developed. However, the previous trust models in distributed networks ignore a fact that the entity itself could provide trust information for services also. In this paper, we present a novel trust management model based on the subjective logic trust for service-oriented distributed networks. The proposed algorithm involves passive trust of entity and combines the direct trust and recommendation trust. We also propose a novel scheme called passive trust feedback to avoid the deceit of malicious nodes and unstable nodes, and to encourage honest nodes. Our proposed model expands the trust range of service resource via passive trust of entity, which generates a flexible access path to the service resources. Simulations show that the proposed trust management model can significantly improve the feasibility of trust management as well as effectively detect malicious entities. Copyright © 2012 John Wiley & Sons, Ltd.

Bilevel decision addresses compromises between two interacting decision entities within a given hierarchical complex system under distributed environments. Bilevel programming typically solves bilevel decision problems. However, formulation of objectives and constraints in mathematical functions is required, which are difficult, and sometimes impossible, in real-world situations because of various uncertainties. Our study develops a rule-set based bilevel decision approach, which models a bilevel decision problem by creating, transforming and reducing related rule sets. This study develops a new rule-sets based solution algorithm to obtain an optimal solution from the bilevel decision problem described by rule sets. A case study and a set of experiments illustrate both functions and the effectiveness of the developed algorithm in solving a bilevel decision problem. Copyright © 2012 John Wiley & Sons, Ltd.

We present the parallelization of a sparse grid finite element discretization of the Black–Scholes equation, which is commonly used for option pricing. Sparse grids allow to handle higher dimensional options than classical approaches on full grids and can be extended to a fully adaptive discretization method. We introduce the algorithmical structure of efficient algorithms operating on sparse grids and demonstrate how they can be used to derive an efficient parallelization with OpenMP of the Black–Scholes solver. We show results on different commodity hardware systems based on multi-core architectures with up to 24 cores and discuss the parallel performance using Intel and Advanced Micro Devices (AMD) CPUs. Copyright © 2012 John Wiley & Sons, Ltd.

As radio frequency identification (RFID) tags become more ubiquitously available, they will stay in dynamic environments where tags can freely enter or leave RFID readers' interrogation range. With such a dynamic tag population, there arises a problem of population monitoring, whose purpose is to identify the missing tags that have departed from the reading range and the new tags that have newly entered. This problem is a new problem which cannot be well solved by the conventional tag identification protocols. In this paper, we first show that this traditional approach is inefficient, because it collects all the tag IDs in each scan and ignores the ready-for-use knowledge of the tag population in a previous scan. To be more efficient, we present three protocols: (i) a baseline protocol that improves the traditional tag identification protocol by optimizing its length of random number used for collision detection; (ii) a novel one-phase protocol with easy labor to identify exactly the new tags and the missing tags by fully utilizing the knowledge of previous tag population; and (iii) a hybrid protocol that smartly combines the baseline protocol and the one-phase protocol. Its purpose is to deal with the situation that the knowledge of previous tag population is highly inconsistent with the current tag population. This hybrid protocol, as shown by our analysis, can improve the tag monitoring accuracy by 25%, and improve the time efficiency by 55.3%, as compared with a recent work (called two-phase protocol), which also identifies the population changes. Copyright © 2012 John Wiley & Sons, Ltd.

Signcryption is a cryptographic primitive that performs simultaneously both the functions of digital signature and public-key encryption, at a cost significantly lower than that required by the traditional signature- then-encryption approach. In this paper, we provide a positive answer to the question of if it is possible to construct signcryption based on lattice problems. More precisely, we design an efficient signcryption scheme that can send a message of length l one time. We prove that the proposed scheme has the indistinguishability against adaptive chosen ciphertext attacks under the learning with errors assumption and strong unforgeability against adaptive chosen messages attacks under the inhomogeneous small integer solution assumption in the random oracle model. Copyright © 2012 John Wiley & Sons, Ltd.

The acceleration of an option pricing technique based on Fourier cosine expansions on the graphics processing unit (GPU) is reported. European options, in particular with multiple strikes, and Bermudan options will be discussed. The influence of the number of terms in the Fourier cosine series expansion, the number of strikes, as well as the number of exercise dates for Bermudan options is explored. We also give details about the different ways of implementing on a GPU. Numerical examples include asset price processes on the basis of a Lévy process of infinite activity and the stochastic volatility Heston model. Furthermore, we discuss the issue of precision on the present GPU systems. Copyright © 2012 John Wiley & Sons, Ltd.

Cognitive radio (CR) can significantly alleviate the network pressure caused by the rapid development of wireless communications through allowing secondary users (SUs) to obtain spectrum resource from primary users (PUs). One key issue of CR technology is spectrum sharing, that is, how spectrum should be allocated between SUs without causing interference to PUs. In this paper, we propose a bilateral bargaining mechanism to achieve this goal between two SUs. The general network scenario with multiple SUs can be decomposed into multiple pairs of bilateral bargaining studied in this paper. The SUs have to reach a mutual satisfactory agreement on the partition of spectrum by making alternating offers to each other. We model such bargaining process as dynamic finite/infinite horizon multistage game with observed actions and fully characterize the corresponding subgame perfect equilibria. Moreover, theoretical analysis and numerical results indicate that our proposed scheme can effectively allocate spectrum resource between SUs. Copyright © 2012 John Wiley & Sons, Ltd.

Although the wireless relay transmission has been recognized as one of the effective approaches in extending the coverage area and improving the network capacity, the delay control over the two-hop wireless channels toward end users’ QoS satisfaction is still challenging in green wireless network designs. In this paper, we propose the QoS-driven delay control schemes over two-hop wireless relay links to statistically upper bound the total queuing delay for the decode-and-forward relay transmissions. Specifically, we first conduct asymptotic queuing-delay analyses for the relay transmissions. Then, we show that an efficient approach for statistical delay guarantees is to make the exponentially decaying speed of the complementary cumulative distribution functions of the queueing delay at both hops identical. We further derive that this target needs to be attained through asymmetric resource allocations over the two hops. Motivated by this fact, we formulate two optimization problems aiming at satisfying the specified delay-bound violation probability for the relay transmission, while minimizing the power consumption toward green wireless networking. One framework is for the frequency-division duplex relay mode and the other is for time-division duplex relay mode. In the frequency-division duplex relay mode, the source and relay nodes use a different transmit power. For the time-division duplex relay mode, the source and relay nodes use time-slots with different length but with the same instantaneous power. We solve for the optimal solutions for both of the aforementioned frameworks. Also conducted is a set of simulation results to show the impact of the QoS requirements, traffic load, and position of the relay node on the resource allocation over wireless relay links. Copyright © 2012 John Wiley & Sons, Ltd.

With distinct advantages in resolving the problems of small sample, nonlinear, high dimension learning, the support vector machine (SVM) has been widely applied in face detection and face recognition. In fact, a large number of facial images were needed to train the SVM algorithms. With the rising of training image numbers, the training complexity of SVM was increased by way of geometric series. In this paper, the hybrid Monte Carlo method of the Bayesian support vector machine is proposed. This method solves the problems of high-dimension and long training time effectively. Experimental results show that the method greatly reduces the training time of face detection algorithm and obtains more accurate face detection effect. Copyright © 2012 John Wiley & Sons, Ltd.

Because of the increasing energy consumption in radio access networks, a new radio access network called Cloud radio access network (C-RAN) has attracted increasing attention. In C-RAN, the distributed radio remote units (RRUs) are located in different sites and the placement of RRUs will influence the system performance. However, few researches investigate the optimal RRU placement from the perspective of energy efficiency, which is one of the most important characteristics of C-RAN. In this paper, we first derive the energy efficiency of C-RAN to achieve certain QoS requirements considering the circuitry energy consumption. Then, we obtain the optimal placement of RRUs by minimizing the consumed energy per bit. Numerical results are also presented to validate the analysis. Copyright © 2012 John Wiley & Sons, Ltd.

This paper investigates the problem of multiuser multimedia communication over orthogonal frequency-division multiple-access downlink systems. A cross-layer design is proposed to maximize the received video quality of all the users subject to the network resource constraint. With the optimal joint subcarrier assignment and power allocation, the proposed scheme can maximally satisfy the requirements of the packet scheduling from the higher layer. Employing the Lagrange dual decomposition method, we can obtain the global optimal solution to the optimization problem. Simulation results show that the proposed algorithm has superior performance compared with the existing alternatives. Copyright © 2012 John Wiley & Sons, Ltd.

In cognitive radio networks (CRNs), considering the randomness of primary users' (PUs) arrival and the nonideality of spectrum sensing performed by secondary users (SUs), the collisions between PUs and SUs are unavoidable. Frequent occurrences of collisions will strongly degrade PUs' and SUs' QoS. Therefore, collision statistics, such as the average number of collisions, is a very important performance metric in CRNs. If collision statistics are not considered in the scheduling scheme of the CRNs, some SUs will meet more collisions than the others, which cause unfair experiences among the SUs. Therefore, a fair scheduling scheme based on collision statistics is proposed in this paper, which can improve fairness across all SUs. First, the number of collisions is defined as an important fairness metric for each SU, and the scheduler dynamically adjusts the priorities of the SUs by periodically counting each SU's collision number. Then, a prediction algorithm, based on the continuous-time Markov chain model, is proposed to predict the idle probabilities of the available channels in the next slot. Considering both the priorities of the SUs and the idle probabilities of the available channels, a rational scheduling scheme will be achieved finally. Assuming the ordered hunt scheduling scheme applied by the primary system, the simulation results show that the proposed scheme can significantly improve the fairness across all SUs with little impact on spectrum utilization. Copyright © 2012 John Wiley & Sons, Ltd.

Because of the presence of incumbents in cognitive radio networks, the unused spectrum in the TV bands, popularly referred to as ‘white spaces’, are fragmented with the size of each fragment varying from one TV channel to several TV channels. What is more, because the secondary transmissions adjust their spectrum usage over time, white spaces become increasingly partitioned into a collection of discrete fragments, which decreases the spectral utilization. To improve throughputs, most of the prior researches focused on selecting the best transmission channel in the context of spectrum fragmentation but have rarely involved aggregating the fragmentation to a contiguous channel. In this paper, we present two adaptive spectrum access strategies, both of which not only select the best transmission channel but also efficiently solve the fragmentation problem. The first strategy involves one-agile radios that build a transmission using single fragment of frequency, which partially remedy the fragmentation problem using higher-layer solutions. The second strategy suppresses the impact of spectrum fragmentation successfully at the physical layer by combining k spectrum fragments to form a single transmission. The simulation results show that both of the strategies bring larger throughputs compared with the prior solutions. Copyright © 2012 John Wiley & Sons, Ltd.

Although the demand for battery capacity on mobile devices has grown with the increase in high data rate applications, battery technology has not kept up with this demand. Therefore, the growth in energy demand coupled with global warming provide a new trend in wireless communication known as energy-efficient transmission. In this paper, the energy-efficient resource allocation for a two-hop uplink multiuser relay-based system is studied. We adopt the orthogonal frequency division multiplexing as the physical layer modulation technique. Assuming that the base station has all the channel state information, an energy efficiency optimization problem by joint subcarrier assignment, bit and power allocation is formulated. We first develop a near-optimal resource allocation scheme to maximize the overall energy efficiency; then, an efficient resource allocation algorithm is provided to solve the problem with relatively low computational complexity. Furthermore, fairness constraint among users is imposed on the system to guarantee each user's QoS. Our joint resource allocation algorithm is proved to achieve a considerable improvement in terms of energy-saving and simultaneously decreases outage probability by simulation results. Copyright © 2012 John Wiley & Sons, Ltd.

Dynamic spectrum auction (DSA) has been considered as one of potential spectrum allocation approaches in cognitive femtocell networks. As a modified version of traditional spectrum auction, DSA should not only increase auction revenue but also improve spectrum utilization on finer time granularity. We propose a DSA algorithm based on a double optimization framework (DOF), which focuses on the optimization of auction revenue and spectrum utilization. The optimization processing consists of two stages. Firstly, a proper auction period is selected to balance the expected spectrum utilization and auction revenue. Then, the cognitive femtocell base station adjusts its reserve price with the repetition of auction to leverage over instant revenue and spectrum utilization. At the same time, the bidders can adjust their bidding price to improve utilities. Performance analysis shows that the DOF-based DSA algorithm has low complexity and can resist collusion, so it can be carried out frequently with small overhead. On the other hand, it is better than the greedy algorithm and Vickrey–Clarke–Groves auction on revenue. Simulation results show that the DOF-based DSA algorithm can keep a fine spectrum utilization and bring the cognitive femtocell base station more revenue in both single-unit award spectrum auction and multi-unit awards spectrum auction. Copyright © 2012 John Wiley & Sons, Ltd.