International Journal of Network Management

Cover image for Vol. 26 Issue 3

Edited By: James Won-Ki Hong

Impact Factor: 0.283

ISI Journal Citation Reports © Ranking: 2014: 73/77 (Telecommunications); 132/139 (Computer Science Information Systems)

Online ISSN: 1099-1190

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International Journal of Network Management

Modern computer networks and communication systems are increasing in size, scope, and heterogeneity. The promise of a single end-to-end technology has not been realized and likely never will occur. The decreasing cost of bandwidth is increasing the possible applications of computer networks and communication systems to entirely new domains. Problems in integrating heterogeneous wired and wireless technologies, ensuring security and quality of service, and reliably operating large-scale systems including the inclusion of cloud computing have all emerged as important topics.

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Recently Published Articles

  1. Online traffic prediction in the cloud

    Bruno L. Dalmazo, João P. Vilela and Marilia Curado

    Version of Record online: 18 MAY 2016 | DOI: 10.1002/nem.1934

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    In this paper, we propose a mechanism based on a dynamic window size algorithm that facilitates online traffic prediction in highly volatile environments as cloud computing. This mechanism improves the accuracy of several traffic predictors, and it decreases the workload and time consumption for predicting network traffic. We evaluate the effectiveness of the solution with real traces from Dropbox and a cloud data center.

  2. Recent advances delivered by Mobile Cloud Computing and Internet of Things for Big Data applications: a survey

    Christos Stergiou and Kostas E. Psannis

    Version of Record online: 5 MAY 2016 | DOI: 10.1002/nem.1930

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    Internet of Things is a new technology that is growing rapidly in the field of telecommunications and especially in the modern field of wireless telecommunications. Based on the technology of wireless networks, both the technologies of Mobile Cloud Computing and Internet of Things develop rapidly. In this paper, we combine the two aforementioned technologies with the Big Data in order to examine the common features and discover which of the Mobile Cloud Computing and Internet of Things benefits improve the use of the Big Data applications.

  3. Software-defined network-based prioritization to avoid video freezes in HTTP adaptive streaming

    Stefano Petrangeli, Tim Wauters, Rafael Huysegems, Tom Bostoen and Filip De Turck

    Version of Record online: 27 APR 2016 | DOI: 10.1002/nem.1931

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    This article presents an OpenFlow-based framework to increase the qual ity of experience of HTTP adaptive streaming (HAS) clients by reducing video freezes. Based on the network conditions and a network-based prediction of the HAS clients' status, an OpenFlow controller prioritizes the delivery of particular HAS segments to avoid freezes at the clients. Emulation in several network scenarios shows how the proposed approach can reduce freezes up to 10 times compared with state-of-the-art solutions, without impacting the performance of cross-traffic applications.

  4. Internet traffic classification based on flows' statistical properties with machine learning

    Alina Vlăduţu, Dragoş Comăneci and Ciprian Dobre

    Version of Record online: 13 APR 2016 | DOI: 10.1002/nem.1929

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    Machine learning for traffic classification is an interesting alternative to deep packet inspection techniques. In this direction, we present a method to classify network traffic using machine learning. We are able to automatically extract flows out of a traffic capture and find relevant statistical property, which are further fed into our proposed unsupervised learning mechanisms for classification purposes.The classification is used as training input for a supervised learning engine that determines different classes of new and unseen traffic flows.

  5. ROUTE: run-time robust reducer workload estimation for MapReduce (pages 224–244)

    Zhihong Liu, Qi Zhang, Raouf Boutaba, Yaping Liu and Zhenghu Gong

    Version of Record online: 29 MAR 2016 | DOI: 10.1002/nem.1928

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    In this paper, we present ROUTE, a run-time robust reducer workload estimation technique for MapReduce. ROUTE progressively samples the partition size of the early completed mappers, allowing ROUTE to perform estimation at run time yet fulfilling the accuracy requirement specified by users. Moreover, by using robust estimation and bootstrapping resampling techniques, ROUTE can achieve high applicability to a wide variety of applications.