IEICE Transactions on Communications
Online ISSN : 1745-1345
Print ISSN : 0916-8516

This article has now been updated. Please use the final version.

Separating Predictable and Unpredictable Flows via Dynamic Flow Mining for Effective Traffic Engineering
Yousuke TAKAHASHIKeisuke ISHIBASHIMasayuki TSUJINONoriaki KAMIYAMAKohei SHIOMOTOTatsuya OTOSHIYuichi OHSITAMasayuki MURATA
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JOURNAL RESTRICTED ACCESS Advance online publication

Article ID: 2017EBT0001

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Abstract

To efficiently use network resources, internet service providers need to conduct traffic engineering that dynamically controls traffic routes to accommodate traffic change with limited network resources. The performance of traffic engineering (TE) depends on the accuracy of traffic prediction. However, the size of traffic change has been drastically increasing in recent years due to the growth in various types of network services, which has made traffic prediction difficult. Our approach to tackle this issue is to separate traffic into predictable and unpredictable parts and to apply different control policies. However, there are two challenges to achieving this: dynamically separating traffic according to predictability and dynamically controlling routes for each separated traffic part. In this paper, we propose a macro ow-based TE scheme that uses different routing policies in accordance with traffic predictability. We also propose a traffic-separation algorithm based on real-time traffic analysis and a framework for controlling separated traffic with software-defined networking technology, particularly OpenFlow. An evaluation of actual traffic measured in an Internet2 network shows that compared with current TE schemes the proposed scheme can reduce the maximum link load by 34% (at the most congested time) and the average link load by an average of 11%.

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© 2017 The Institute of Electronics, Information and Communication Engineers
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