Metric Ranking of Invariant Networks with Belief Propagation

Changxia Tao, Yong Ge, Qinbao Song, Yuan Ge, Olufemi A. Omitaomu

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

8 Scopus citations

Abstract

The management of large-scale distributed information systems relies on the effective use and modeling of monitoring data collected at various points in the distributed information systems. A promising approach is to discover invariant relationships among the monitoring data and generate invariant networks, where a node is a monitoring data source (metric) and a link indicates an invariant relationship between two monitoring data. Such an invariant network representation can help system experts to localize and diagnose the system faults by examining those broken invariant relationships and their related metrics, because system faults usually propagate among the monitoring data and eventually lead to some broken invariant relationships. However, at one time, there are usually a lot of broken links (invariant relationships) within an invariant network. Without proper guidance, it is difficult for system experts to manually inspect this large number of broken links. Thus, a critical challenge is how to effectively and efficiently rank metrics (nodes) of invariant networks according to the anomaly levels of metrics. The ranked list of metrics will provide system experts with useful guidance for them to localize and diagnose the system faults. To this end, we propose to model the nodes and the broken links as a Markov Random Field (MRF), and develop an iteration algorithm to infer the anomaly of each node based on belief propagation (BP). Finally, we validate the proposed algorithm on both real-world and synthetic data sets to illustrate its effectiveness.

Original languageEnglish (US)
Title of host publicationProceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
EditorsRavi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1001-1006
Number of pages6
EditionJanuary
ISBN (Electronic)9781479943029
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
Event14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China
Duration: Dec 14 2014Dec 17 2014

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
NumberJanuary
Volume2015-January
ISSN (Print)1550-4786

Conference

Conference14th IEEE International Conference on Data Mining, ICDM 2014
Country/TerritoryChina
CityShenzhen
Period12/14/1412/17/14

Keywords

  • ARX Model
  • Belief Propogation
  • Invariant
  • Invariant Networks

ASJC Scopus subject areas

  • Engineering(all)

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