Context change detection for resource allocation in service-oriented systems

Piotr Rygielski*, Jakub M. Tomczak

*Corresponding author for this work

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

Abstract

In this paper, the problem of detecting the major changes in the stream of service requests is formulated. The change of stream component varies over time and depends on, e.g., a time of a day. The underlying cause of the change is called a context. Hence, at each moment there exists a probability distribution determining the probability of requesting the system service conditioned by the context. The aim is to find such a moment in which the distributions change. To solve that problem dissimilarity measures between two probability distributions are given. Nevertheless, detecting every change is not interesting but only long-lasting changes because of the costs of the service system resources reallocation. Therefore, in the proposed algorithm an issue of sensitivity to temporary changes detection is considered.

Original languageEnglish
Title of host publicationKnowledge-Based and Intelligent Information and Engineering Systems - 15th International Conference, KES 2011, Proceedings
Pages591-600
Number of pages10
EditionPART 2
DOIs
Publication statusPublished - 29 Sept 2011
Externally publishedYes
Event15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011 - Kaiserslautern, Germany
Duration: 12 Sept 201114 Sept 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6882 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011
Country/TerritoryGermany
CityKaiserslautern
Period12/09/1114/09/11

Funding

Acknowledgments. The research presented in this paper has been partially supported by the European Union within the European Regional Development Fund program no. POIG.01.03.01-00-008/08. The research has been also partially co-financed by European Union within European Social Fund.

Keywords

  • datastream
  • dissimilarity measure
  • sliding window estimation

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