Adaptive random sampling for load change detection

Baek Young Choi, Jaesung Park, Zhi Li Zhang

Research output: Contribution to journalConference articlepeer-review

46 Scopus citations


Timely detection of changes in traffic load is critical for initiating appropriate traffic engineering mechanisms. Accurate measurement of traffic is essential since the efficacy of change detection depends on the accuracy of traffic estimation. However, precise traffic measurement involves inspecting every packet traversing a link, resulting in significant overhead, particularly on high speed links. Sampling techniques for traffic load estimation are proposed as a way to limit the measurement overhead. In this paper, we address the problem of bounding sampling error within a pre-specified tolerance level and propose an adaptive random sampling technique that determines the minimum sampling probability adaptively according to traffic dynamics. Using real network traffic traces, we show that the proposed adaptive random sampling technique indeed produces the desired accuracy, while also yielding significant reduction in the amount of traffic samples. We also investigate the impact of sampling errors on the performance of load change detection.

Original languageEnglish (US)
Pages (from-to)272-273
Number of pages2
JournalPerformance Evaluation Review
Issue number1
StatePublished - 2002
EventACM SIGMETRICS 2002 International Conference on Measurement and Modeling of Computer Systems - Marina Del Rey, CA, United States
Duration: Jun 15 2002Jun 19 2002


  • Change detection
  • Sampling


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