NetCSI: A Generic Fault Diagnosis Algorithm for Large-Scale Failures in Computer Networks

Srikar Tati, Scott Rager, Bong Jun Ko, Guohong Cao, Ananthram Swami, Thomas F. La Porta

Research output: Contribution to journalArticle

Abstract

We present a framework and a set of algorithms for determining faults in networks when large scale outages occur. The design principles of our algorithm, netCSI, are motivated by the fact that failures are geographically clustered in such cases. We address the challenge of determining faults with incomplete symptom information due to a limited number of reporting nodes. netCSI consists of two parts: a hypotheses generation algorithm, and a ranking algorithm. When constructing the hypothesis list of potential causes, we make novel use of positive and negative symptoms to improve the precision of the results. In addition, we propose pruning and thresholding along with a dynamic threshold value selector, to reduce the complexity of our algorithm. The ranking algorithm is based on conditional failure probability models that account for the geographic correlation of the network objects in clustered failures. We evaluate the performance of netCSI for networks with both random and realistic topologies. We compare the performance of netCSI with an existing fault diagnosis algorithm, MAX-COVERAGE, and demonstrate an average gain of 128 percent in accuracy for realistic topologies.

Original languageEnglish (US)
Article number6951396
Pages (from-to)355-368
Number of pages14
JournalIEEE Transactions on Dependable and Secure Computing
Volume13
Issue number3
DOIs
Publication statusPublished - May 1 2016

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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