SAX: A Tool for Studying Congestion-Induced Surfer Behavior

نویسندگان

  • Dinh Nguyen Tran
  • Wei Tsang
  • Y. C. Tay
چکیده

User reaction to traffic congestion can have severe impact on network stability and significant implication for traffic engineering. For example, users who persist in large peer-to-peer transfers despite congestion can drive the network into congestion collapse. On the other hand, users who abort large transfers can smoothen self-similar traffic. We present a tool, called SAX, for studying congestion-induced behavior of web surfers. SAX extracts information from HTTP packet traces and infers clicks, abortions and sessions. Measurements with SAX show how users back-off when congestion occurs.

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تاریخ انتشار 2006