Using quantitative analysis to implement autonomic IT systems

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 framewo...

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Veröffentlicht in:2009 IEEE 31st International Conference on Software Engineering S. 100 - 110
Hauptverfasser: Calinescu, Radu, Kwiatkowska, Marta
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Washington, DC, USA IEEE Computer Society 16.05.2009
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
Computer architecture
Computing methodologies > Modeling and simulation > Model development and analysis > Modeling methodologies
Disk drives
Energy consumption
Energy management
Knowledge management
Logic devices
Mathematics of computing > Probability and statistics > Probabilistic representations > Markov networks
Mathematics of computing > Probability and statistics > Stochastic processes > Markov processes
Pervasive computing
Power system management
Probabilistic logic
Runtime
Software and its engineering > Software creation and management > Designing software > Software implementation planning > Software design techniques
Software and its engineering > Software creation and management > Software development process management
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Markov decision processes
ISBN:9781424434534, 142443453X
ISSN:0270-5257
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
ISBN:9781424434534
142443453X
ISSN:0270-5257
DOI:10.1109/ICSE.2009.5070512