Using quantitative analysis to implement autonomic IT systems

Radu Calinescu; Marta Kwiatkowska

doi:10.1109/ICSE.2009.5070512

Using quantitative analysis to implement autonomic IT systems

Radu Calinescu, Marta Kwiatkowska

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the 31st International Conference on Software Engineering (ICSE)
Pages	100-110
Number of pages	11
DOIs	https://doi.org/10.1109/ICSE.2009.5070512
Publication status	Published - 2009

Bibliographical note

Google Scholar citations on 17 October 2012: 45

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10.1109/ICSE.2009.5070512

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5070512

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AB - 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.

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BT - Proceedings of the 31st International Conference on Software Engineering (ICSE)

ER -

Using quantitative analysis to implement autonomic IT systems

Abstract

Bibliographical note

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