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Using Non-random Associations for Predicting Latency in WANs

  • Vladimir Zadorozhny
  • Louiqa Raschid
  • Avigdor Gal
  • Qiang Ye
  • Hyma Murthy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3806)

Abstract

In this paper, we propose a scalable performance management tool for Wide Area Applications. Our objective is to scalably identify non-random associations between pairs of individual Latency Profiles (iLPs) (i.e., latency distributions experienced by clients when connecting to a server) and exploit them in latency prediction. Our approach utilizes Relevance Networks (RNs) to manage tens of thousands of iLPs. Non-random associations between iLPs can be identified by topology-independent measures such as correlation and mutual information. We demonstrate that these non-random associations do indeed have a significant impact in improving the error of latency prediction.

Keywords

Mutual Information Border Gateway Protocol Correlation Threshold Relevance Network Latency Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Vladimir Zadorozhny
    • 1
  • Louiqa Raschid
    • 2
  • Avigdor Gal
    • 3
  • Qiang Ye
    • 1
  • Hyma Murthy
    • 2
  1. 1.University of PittsburghPittsburgh
  2. 2.University of MarylandCollege Park
  3. 3.Israel Institute of TechnologyHaifaIsrael

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