Agent-Mining of Grid Log-Files: A Case Study

Arjan J.R. Stoter, Simon Dalmolen, Wico Mulder

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

Abstract

Grid monitoring requires analysis of large amounts of log files across multiple domains. An approach is described for automated extraction of job-flow information from large computer grids, using software agents and genetic computation. A prototype was created as a first step towards communities of agents that will collaborate to learn log-file structures and exchange knowledge across organizational domains
Original languageEnglish
Title of host publicationAgents and Data Mining Interaction
Subtitle of host publication8th International Workshop, ADMI 2012, Valencia, Spain, June 4-5, 2012, Revised Selected Papers
EditorsLongbing Cao, Yifeng Zeng, Andreas L. Symeonidis
Place of PublicationBerlin, Heidelberg
PublisherSpringer
Pages166-177
ISBN (Electronic)978-3-642-36288-0
ISBN (Print)978-3-642-36287-3
DOIs
Publication statusPublished - 4 Jun 2013
Event8th International Workshop on Agents and Data Mining Interaction, ADMI 2012 - Valencia, Spain
Duration: 4 Jun 20125 Jun 2012
Conference number: 8

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume7607
ISSN (Print)0302-9743
NameLecture Notes in Artificial Intelligence
PublisherSpringer

Conference

Conference8th International Workshop on Agents and Data Mining Interaction, ADMI 2012
Abbreviated titleADMI
CountrySpain
CityValencia
Period4/06/125/06/12

Keywords

  • Grid monitoring
  • Text mining
  • Agent oriented programming
  • Genetic computation
  • Engineering

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  • Cite this

    Stoter, A. J. R., Dalmolen, S., & Mulder, W. (2013). Agent-Mining of Grid Log-Files: A Case Study. In L. Cao, Y. Zeng, & A. L. Symeonidis (Eds.), Agents and Data Mining Interaction: 8th International Workshop, ADMI 2012, Valencia, Spain, June 4-5, 2012, Revised Selected Papers (pp. 166-177). (Lecture notes in computer science; Vol. 7607), (Lecture Notes in Artificial Intelligence). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-36288-0_15