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End-to-end estimation of the available bandwidth variation range

Published:06 June 2005Publication History
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Abstract

The available bandwidth (avail-bw) of a network path is an important performance metric and its end-to-end estimation has recently received significant attention. Previous work focused on the estimation of the average avail-bw, ignoring the significant variability of this metric in different time scales. In this paper, we show how to estimate a given percentile of the avail-bw distribution at a user-specified time scale. If two estimated percentiles cover the bulk of the distribution (say 10% to 90%), the user can obtain a practical estimate for the avail-bw variation range. We present two estimation techniques. The first is iterative and non-parametric, meaning that it is more appropriate for very short time scales (typically less than 100ms), or in bottlenecks with limited flow multiplexing (where the avail-bw distribution may be non-Gaussian). The second technique is parametric, because it assumes that the avail-bw follows the Gaussian distribution, and it can produce an estimate faster because it is not iterative. The two techniques have been implemented in a measurement tool called Pathvar. Pathvar can track the avail-bw variation range within 10-20%, even under non-stationary conditions. Finally, we identify four factors that play a crucial role in the variation range of the avail-bw: traffic load, number of competing flows, rate of competing flows, and of course the measurement time scale.

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    • Published in

      cover image ACM SIGMETRICS Performance Evaluation Review
      ACM SIGMETRICS Performance Evaluation Review  Volume 33, Issue 1
      Performance evaluation review
      June 2005
      417 pages
      ISSN:0163-5999
      DOI:10.1145/1071690
      Issue’s Table of Contents
      • cover image ACM Conferences
        SIGMETRICS '05: Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
        June 2005
        428 pages
        ISBN:1595930221
        DOI:10.1145/1064212

      Copyright © 2005 ACM

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      • Published: 6 June 2005

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