Traffic Matrix Estimation Using Spike Flow Detection(<Special Section>Internet Technology V)

• SHIMIZU Susumu

  NTT Network Innovation Laboratories, NTT Corporation School of Science and Engineering, Waseda University

• FUKUDA Kensuke

  NTT Network Innovation Laboratories, NTT Corporation

• MURAKAMI Kenichiro

  NTT Network Innovation Laboratories, NTT Corporation

• GOTO Shigeki

  School of Science and Engineering, Waseda University

抄録

This paper proposes a new method of estimating real-time traffic matrices that only incurs small errors in estimation. A traffic matrix represents flows of traffic in a network. It is an essential tool for capacity planning and traffic engineering. However, the high costs involved in measurement make it difficult to assemble an accurate traffic matrix. It is therefore important to estimate a traffic matrix using limited information that only incurs small errors. Existing approaches have used IP-related information to reduce the estimation errors and computational complexity. In contrast, our method, called spike flow measurement (SFM) reduces errors and complexity by focusing on spikes. A spike is transient excessive usage of a communications link. Spikes are easily monitored through an SNMP framework. This reduces the measurement costs compared to that of other approaches. SFM identifies spike flows from traffic byte counts by detecting pairs of incoming and outgoing spikes in a network. A matrix is then constructed from collected spike flows as an approximation of the real traffic matrix. Our experimental evaluation reveals that the average error in estimation is 28%, which is sufficiently small for the method to be applied to a wide range of network nodes, including Ethernet switches and IP routers.

収録刊行物

• IEICE transactions on communications

  IEICE transactions on communications 88 (4), 1484-1492, 2005-04-01

  一般社団法人電子情報通信学会

キーワード

• network management
• network measurement
• traffic matrix
• spike flow

詳細情報

• CRID: 1573950399999826432
• NII論文ID: 10016563844
• NII書誌ID: AA10826261
• ISSN: 09168516
• 本文言語コード: en
• データソース種別:
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