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Adaptive algorithms for diagnosing large-scale failures in computer networks

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5 Author(s)
Srikar Tati ; Network and Security Research Center, Pennsylvania State University, USA ; Bong Jun Ko ; Guohong Cao ; Ananthram Swami
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In this paper, we propose an algorithm to efficiently diagnose large-scale clustered failures. The algorithm, Cluster-MAX-COVERAGE (CMC), is based on greedy approach. We address the challenge of determining faults with incomplete symptoms. CMC makes novel use of both positive and negative symptoms to output a hypothesis list with a low number of false negatives and false positives quickly. CMC requires reports from about half as many nodes as other existing algorithms to determine failures with 100% accuracy. Moreover, CMC accomplishes this gain significantly faster (sometimes by two orders of magnitude) than an algorithm that matches its accuracy. Furthermore, we propose an adaptive algorithm called Adaptive-MAX-COVERAGE (AMC) that performs efficiently during both kinds of failures, i.e., independent and clustered. During a series of failues that include both independent and clustered, AMC results in a reduced number of false negatives and false positives.

Published in:

IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2012)

Date of Conference:

25-28 June 2012