Separating Predictable and Unpredictable Flows via Dynamic Flow Mining for Effective Traffic Engineering

Yousuke TAKAHASHI  Keisuke ISHIBASHI  Masayuki TSUJINO  Noriaki KAMIYAMA  Kohei SHIOMOTO  Tatsuya OTOSHI  Yuichi OHSITA  Masayuki MURATA  

IEICE TRANSACTIONS on Communications   Vol.E101-B    No.2    pp.538-547
Publication Date: 2018/02/01
Publicized: 2017/08/07
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2017EBT0001
Type of Manuscript: PAPER
Category: Internet
software defined networking,  routing,  traffic engineering,  macroflow,  flow mining,  

Full Text: FreePDF

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 macroflow-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%.