Skip to main navigation Skip to search Skip to main content

Ceaser:Exploring the monitoring rules in the time dimension

  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In cloud environment, most failures would not strike at a sudden moment but evolve from a minor anomaly. During the evolving procedure, it may generate a series of events. But users can't specify exactly the event sequences of all the failures due the lack of time or knowledge. Automatically exploring the monitoring rules in time dimension can help users refine their rules and timely discover the failure. Based on these facts, we introduce our prototype: Ceaser. It analyzes the raw rules that user specified based on history event sequence, and try to refine them in the time dimension. Unlike pattern mining or event summarization, the rule refinement process is handled by a rule generator Ceaser-S and an extensible CEP engine Ceaser-E. The cooperation of these two can not only discover the event sequence pattern which one event happens after another, they can also explore the monitoring rules including the negative pattern or Kleene-closure pattern and other user defined pattern.

Original languageEnglish
Title of host publicationProceedings - 2013 International Conference on Cloud and Service Computing, CSC 2013
PublisherIEEE Computer Society
Pages49-56
Number of pages8
ISBN (Print)9780769551579
DOIs
StatePublished - 2013
Event2013 International Conference on Cloud and Service Computing, CSC 2013 - Beijing, China
Duration: 4 Nov 20136 Nov 2013

Publication series

NameProceedings - 2013 International Conference on Cloud and Service Computing, CSC 2013

Conference

Conference2013 International Conference on Cloud and Service Computing, CSC 2013
Country/TerritoryChina
CityBeijing
Period4/11/136/11/13

Keywords

  • CEP
  • Event sequences
  • Monitoring
  • System management
  • Temporal dependency

Fingerprint

Dive into the research topics of 'Ceaser:Exploring the monitoring rules in the time dimension'. Together they form a unique fingerprint.

Cite this