Big Data Science

Traffic flow aggregation for traffic engineering

- Classifying flows based on traffic variation pattern -

Abstract

Although the use of software-defined networking (SDN) enables routes of packets to be controlled with finer granularity by using traffic engineering (TE), the corresponding increase in the number of states that need to be managed at routers and controller is problematic in large-scale networks. Aggregating flows into macro flows and assigning routes by macro flow should be an effective approach to solving this problem. However, when macro flows are constructed as TE targets, variations of traffic rates in each macro flow should be minimized to improve route stability. We propose to cluster micro flows with similar traffic variation patterns into groups and optimizes the traffic ratio of extracted from each cluster to aggregate into each macro flow.

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Poster


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Presenters

Noriaki Kamiyama
Network Technology Laboratories
Takahashi Yousuke
Network Technology Laboratories
Keisuke Ishibashi
Keisuke Ishibashi
Network Technology Laboratories