Cross-Domain Fault Localization: A Case for a Graph Digest Approach

Abstract

Prior research has focused on intra-domain fault localization leaving the cross-domain problem largely unaddressed. Faults often have widespread effects, which if correlated, could significantly improve fault localization. Past efforts rely on probing techniques or assume hierarchical domain structures; however, administrators are often unwilling to share network structure and state and domains are organized and connected in complex ways. We present an inference-graph-digest based formulation of the problem. The formulation not only explicitly models the inference accuracy and privacy requirements for discussing and reasoning over cross-domain problems, but also facilitates the re-use of existing fault localization algorithms while enforcing domain privacy policies. We demonstrate our formulation by deriving a cross-domain version of SHRINK, a recent probabilistic fault localization strategy.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2008
Accession Number
ADA487214

Entities

People

  • Geoffrey G. Xie
  • Joel D. Young
  • William D. Fischer

Organizations

  • Naval Postgraduate School

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Bayesian Inference
  • Bayesian Networks
  • Circuits
  • Communication Channels
  • Communication Systems
  • Computer Communications
  • Cross Domain
  • Models
  • Network Architecture
  • Network Protocols
  • Networks
  • Neural Networks
  • Operations Research
  • Probability
  • Probability Distributions

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Cybersecurity.
  • Fault Tolerant Diagnosis of Black and White Balloon Isolation Tests Using ¥.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks