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Reinforcement Learning Techniques for Decentralized Self-adaptive Service Assembly
Linnaeus University, Faculty of Technology, Department of Computer Science.ORCID iD: 0000-0001-6981-0966
Linnaeus University, Faculty of Technology, Department of Computer Science.
Univ Roma Tor Vergata, Italy.
Politecn Milan, Italy..
2016 (English)In: SERVICE-ORIENTED AND CLOUD COMPUTING, (ESOCC 2016), 2016, 53-68 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a self-organizing fully decentralized solution for the service assembly problem, whose goal is to guarantee a good overall quality for the delivered services, ensuring at the same time fairness among the participating peers. The main features of our solution are: (i) the use of a gossip protocol to support decentralized information dissemination and decision making, and (ii) the use of a reinforcement learning approach to make each peer able to learn from its experience the service selection rule to be followed, thus overcoming the lack of global knowledge. Besides, we explicitly take into account load-dependent quality attributes, which lead to the definition of a service selection rule that drives the system away from overloading conditions that could adversely affect quality and fairness. Simulation experiments show that our solution self-adapts to occurring variations by quickly converging to viable assemblies maintaining the specified quality and fairness objectives.

Place, publisher, year, edition, pages
2016. 53-68 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9846
National Category
Computer and Information Science
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-59473DOI: 10.1007/978-3-319-44482-6_4ISI: 000388798100004ISBN: 978-3-319-44482-6 (print)ISBN: 978-3-319-44481-9 (print)OAI: oai:DiVA.org:lnu-59473DiVA: diva2:1059760
Conference
5th IFIP WG 2.14 European Conference on Service-Oriented and Cloud Computing (ESOCC), SEP 05-07, 2016, Vienna, AUSTRIA
Available from: 2016-12-23 Created: 2016-12-23 Last updated: 2017-04-21Bibliographically approved

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Caporuscio, MauroD'Angelo, Mirko
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf