Learning, indexing, and diagnosing network faults

Ting Wang, Mudhakar Srivatsa, Dakshi Agrawal, Ling Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

Modern communication networks generate massive volume of operational event data, e.g., alarm, alert, and metrics, which can be used by a network management system (NMS) to diagnose potential faults. In this work, we introduce a new class of indexable fault signatures that encode temporal evolution of events generated by a network fault as well as topological relationships among the nodes where these events occur. We present an efficient learning algorithm to extract such fault signatures from noisy historical event data, and with the help of novel space-time indexing structures, we show how to perform efficient, online signature matching. We provide results from extensive experimental studies to explore the efficacy of our approach and point out potential applications of such signatures for many different types of networks including social and information networks.

Original languageEnglish (US)
Title of host publicationKDD '09
Subtitle of host publicationProceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages857-865
Number of pages9
DOIs
StatePublished - Nov 9 2009
Event15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09 - Paris, France
Duration: Jun 28 2009Jul 1 2009

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09
CountryFrance
CityParis
Period6/28/097/1/09

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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