Using quantitative analysis to implement autonomic IT systems

Radu C. Calinescu, Marta Z. Kwiatkowska

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

Abstract

The software underpinning today’s IT systems needs to adapt dynamically and predictably to rapid changes in system workload, environment and objectives. We describe a software framework that achieves such adaptiveness for IT systems whose components can be modelled as Markov chains. The framework comprises (i) an autonomic architecture that uses Markov-chain quantitative analysis to dynamically adjust the parameters of an IT system in line with its state, environment and objectives; and (ii) a method for developing instances of this architecture for real-world systems. Two case studies are presented that use the framework successfully for the dynamic power management of disk drives, and for the adaptive management of cluster availability within data centres, respectively.
Original languageEnglish
Title of host publicationIEEE 31st International Conference on Software Engineering, 2009. ICSE 2009
PublisherIEEE
Pages100-110
Number of pages11
ISBN (Print)9781424434527
Publication statusPublished - May 2009
Event2009 IEEE 31st International Conference on Software Engineering - Vancouver, Canada
Duration: 16 May 200924 May 2009

Conference

Conference2009 IEEE 31st International Conference on Software Engineering
CountryCanada
CityVancouver
Period16/05/0924/05/09

Fingerprint

Markov processes
Chemical analysis
Availability
Power management

Bibliographical note

© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

  • Markov processes
  • Web services
  • computer centres
  • probability
  • program diagnostics
  • program verification
  • software architecture
  • software fault tolerance
  • software maintenance
  • Markov chain
  • PRISM probabilistic model checker
  • adaptive cluster availability management
  • autonomic legacy IT system
  • autonomic software architecture
  • data centre
  • disk drive
  • dynamic power management
  • quantitative analysis tool
  • software framework

Cite this

Calinescu, R. C., & Kwiatkowska, M. Z. (2009). Using quantitative analysis to implement autonomic IT systems. In IEEE 31st International Conference on Software Engineering, 2009. ICSE 2009 (pp. 100-110). IEEE.
Calinescu, Radu C. ; Kwiatkowska, Marta Z. / Using quantitative analysis to implement autonomic IT systems. IEEE 31st International Conference on Software Engineering, 2009. ICSE 2009. IEEE, 2009. pp. 100-110
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keywords = "Markov processes, Web services, computer centres, probability, program diagnostics, program verification, software architecture, software fault tolerance, software maintenance, Markov chain, PRISM probabilistic model checker, adaptive cluster availability management, autonomic legacy IT system, autonomic software architecture, data centre, disk drive, dynamic power management, quantitative analysis tool, software framework",
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Calinescu, RC & Kwiatkowska, MZ 2009, Using quantitative analysis to implement autonomic IT systems. in IEEE 31st International Conference on Software Engineering, 2009. ICSE 2009. IEEE, pp. 100-110, 2009 IEEE 31st International Conference on Software Engineering, Vancouver, Canada, 16/05/09.

Using quantitative analysis to implement autonomic IT systems. / Calinescu, Radu C.; Kwiatkowska, Marta Z.

IEEE 31st International Conference on Software Engineering, 2009. ICSE 2009. IEEE, 2009. p. 100-110.

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

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Calinescu RC, Kwiatkowska MZ. Using quantitative analysis to implement autonomic IT systems. In IEEE 31st International Conference on Software Engineering, 2009. ICSE 2009. IEEE. 2009. p. 100-110