NDLI: Connection-level analysis and modeling of network traffic

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Connection-level analysis and modeling of network traffic

Content Provider	ACM Digital Library
Author	Riedi, Rudolf Baraniuk, Richard Sarvotham, Shriram
Abstract	Most network traffic analysis and modeling studies lump all connections together into a single flow. Such aggregate traffic typically exhibits long-range-dependent (LRD) correlations and non-Gaussian marginal distributions. Importantly, in a typical aggregate traffic model, traffic bursts arise from many connections being active simultaneously. In this paper, we develop a new framework for analyzing and modeling network traffic that moves beyond aggregation by incorporating connection-level information. A careful study of many traffic traces acquired in different networking situations reveals (in opposition to the aggregate modeling ideal) that traffic bursts typically arise from just a few high-volume connections that dominate all others. We term such dominating connections alpha traffic. Alpha traffic is caused by large file transmissions over high bandwidth links and is extremely bursty (non-Gaussian). Stripping the alpha traffic from an aggregate trace leaves a beta traffic residual that is Gaussian, LRD, and shares the same fractal scaling exponent as the aggregate traffic. Beta traffic is caused by both small and large file transmissions over low bandwidth links. In our alpha/beta traffic model, the heterogeneity of the network resources give rise to burstiness and heavy-tailed connection durations give rise to LRD. Queuing experiments suggest that the alpha component dictates the tail queue behavior for large queue sizes, whereas the beta component controls the tail queue behavior for small queue sizes.
Starting Page	99
Ending Page	103
Page Count	5
File Format	PDF
ISBN	1581134355
DOI	10.1145/505202.505215
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2001-11-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Network traffic modeling Animal kingdom
Content Type	Text
Resource Type	Article

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