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Keywords:

  • CGC2011;
  • cloud computing;
  • green computing

INTRODUCTION

  1. Top of page
  2. INTRODUCTION
  3. REFERENCES

This special issue of Concurrency and Computation: Practice and Experience contains selected high-quality papers from the 2011 International Conference on Cloud and Green Computing (CGC2011) which was held on December 11–13, 2011 in Sydney, Australia [1]. The CGC conference series aims to provide an international forum for the presentation and discussion of research and development trends regarding cloud and green computing. CGC2011 attracted many international attendants, allowing deep discussion and the exchange of ideas and results related to ongoing research among attendants.

Many research and development efforts have been made in the field of cloud and green computing such as [2-11]. More and more people from different areas are trying to facilitate the techniques from their respective areas to tackle tough issues in cloud and green computing such as resource scheduling, security and privacy, service provision, power aware computation and storage, and data service query issues. This special issue aims to accommodate a range of papers from different perspectives and areas to provide some different views and hints for cloud and green computing research.

This special issue contains eight papers based on those that were presented at CGC2011. They are listed as [12-19]. Research problems in these papers have been analyzed systematically, and for specific approaches or models, evaluation has been performed to demonstrate their feasibility and advantages. The papers were selected on this basis and also peer reviewed thoroughly. They are summarized in the succeeding texts.

Paper [12] develops an adaptive service selection method for cross-cloud service composition. It can dynamically select proper services with near-optimal performance for adapting to changes in time. A case study is presented to demonstrate the performance.

Paper [13] attempts to identify the role of contextual properties of enterprise systems architecture in relation to service migration to cloud computing. It points out that cloud computing requires consumers to relinquish their ownership of and control over most architectural elements to cloud providers. The simulation is conducted to evaluate the feasibility of the proposed method.

Paper [14] proposes an economic and energy aware cloud cost model in this regard. The model supports the decision-making process to be applied with business cases and enables cloud consumers and cloud providers to define their own business strategies and to analyze the respective impact on their business.

Paper [15] focuses on latency in global cloud service provision. This paper investigates if latency in terms of simple ping measurements can be used as an indicator for other QoS parameters such as jitter and throughput. Corresponding experiments are conducted to demonstrate performance.

Paper [16] presents a number of policies that can be applied to multiuse clusters where computers are shared between interactive users and high throughput computing. The paper also evaluates policies by trace-driven simulations to determine the effects on power consumed by the high throughput workload and impact on high throughput users. The experiment results demonstrate significant power saving with proposed policies.

Paper [17] designs an efficient data and task co-scheduling strategy for scheduling datasets and tasks together. Simulation was conducted on the well-known Tianhe supercomputer platform. Simulation results demonstrate that the proposed strategy can effectively improve workflows performance while reducing the total volume of data transfer across data centers.

Paper [18] makes an effort in this area of monitoring resources with QoS in cloud environment. The paper then designs a heuristic QoS measurement constructed with domain-based information model. Details are presented in the paper. The experiments are conducted in an implemented portal.

Paper [19] addresses privacy preservation of big data on cloud. The paper proposes a scalable cost-effective framework to efficiently preserve privacy. The motivation is that existing approaches have not considered large-scale cloud environment with distributed big data processing, hence, not scalable for large-scale distributed processing of big data on cloud, which is a natural feature of big data. Corresponding details and evaluation are presented.

REFERENCES

  1. Top of page
  2. INTRODUCTION
  3. REFERENCES
  • 1
  • 2
    Yuan D, Yang Y, Liu X, Chen J. On-demand minimum cost benchmarking for intermediate datasets storage in scientific cloud workflow systems. Journal of Parallel and Distributed Computing 2011; 71(2): 316332.
  • 3
    Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M. A view of cloud computing. Communications of the ACM 2010; 53(4): 5058.
  • 4
    Wang L, Zhan J, Shi W, Liang Y. In cloud, can scientific communities benefit from the economies of scale?. IEEE Transactions on Parallel and Distributed Systems 2012; 23(2): 296303.
  • 5
    Zissis D, Lekkas D. Addressing cloud computing security issues. Future Generation Computer Systems 2011; 28(3): 583592.
  • 6
    Bernd G, Tobias W, Elmar S. Understanding cloud computing vulnerabilities. IEEE Security and Privacy 2010; 9(2): 5057.
  • 7
    Lin H, Tzeng W. A secure erasure code-based cloud storage system with secure data forwarding. IEEE Transactions on Parallel and Distributed Systems 2012; 23(6): 9951003.
  • 8
    Cao N, Wang C, Li M, Ren K, Lou W. Privacy-preserving multi-keyword ranked search over encrypted cloud data. Proceedings of the 31st Annual IEEE International Conference on Computer Communications (INFOCOM'11), April 2011. IEEE CS Press, Los Alamitos, CA, USA, Shanghai, China, 2011; 829837.
  • 9
    Zhang X, Liu C, Nepal S, Pandey S, Chen J. A privacy leakage upper-bound constraint based approach for cost-effective privacy preserving of intermediate datasets in cloud. IEEE Transactions on Parallel and Distributed Systems 2012; 24(6): 11921202.
  • 10
    Zhang X, Yang T, Liu C, Chen J. A scalable two-phase top-down specialization approach for data anonymization using mapreduce on cloud. IEEE Transactions on Parallel and Distributed Systems, in press, accepted on 6 Feb. 2013.
  • 11
    Zhang X, Liu C, Nepal S, Chen J. An efficient quasi-identifier index based approach for privacy preservation over incremental data sets on cloud. Journal of Computer and System Sciences 2012; 79(5): 542555.
  • 12
    Yang J, Lin W, Dou W. An adaptive service selection method for cross-cloud service composition. Concurrency and Computation: Practice and Experience 2013; 25(18): 24352454.
  • 13
    Khan K, Malluhi Q. Role of contextual properties in enterprise service migration to cloud computing. Concurrency and Computation: Practice and Experience 2013; 25(18): 24552470.
  • 14
    Mach W, Schikuta E. Towards an economic and energy aware cloud cost model. Concurrency and Computation: Practice and Experience 2013; 25(18): 24712487.
  • 15
    Pedersen J, Riaz MT, Júnior JC, Dubalski B, Ledzinski D, Patel A. Using latency as a QoS-indicator for global cloud computing services. Concurrency and Computation: Practice and Experience 2013; 25(18): 24882500.
  • 16
    McGough AS, Forshaw M, Gerrard C, Robinson P, Wheater S. Analysis of power-saving techniques over a large multi-use cluster with variable workload. Concurrency and Computation: Practice and Experience 2013; 25(18): 25012522.
  • 17
    Deng K, Ren K, Song J, Yuan D, Xiang Y, Chen J. A clustering based coscheduling strategy for efficient scientific workflow execution in cloud computing. Concurrency and Computation: Practice and Experience 2013; 25(18): 25232539.
  • 18
    Yang C, Liu J, Ranjan R, Shih W, Lin C. On construction of heuristic QoS bandwidth management in clouds. Concurrency and Computation: Practice and Experience 2013; 25(18): 25402560.
  • 19
    Zhang X, Liu C, Nepal S, Yang C, Dou W, Chen J. SaC-FRAPP: A scalable and cost-effective framework for privacy preservation over big data on cloud. Concurrency and Computation: Practice and Experience 2013; 25(18): 25612576.