Using quantitative analysis to implement autonomic IT systems

Radu C. Calinescu; Marta Z. Kwiatkowska

Using quantitative analysis to implement autonomic IT systems

Radu C. Calinescu, Marta Z. Kwiatkowska

Computer Science Research Group

Research output: Chapter in Book/Published conference output › Conference publication

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	IEEE 31st International Conference on Software Engineering, 2009. ICSE 2009
Publisher	IEEE
Pages	100-110
Number of pages	11
ISBN (Print)	9781424434527
Publication status	Published - May 2009
Event	2009 IEEE 31st International Conference on Software Engineering - Vancouver, Canada Duration: 16 May 2009 → 24 May 2009

Conference

Conference	2009 IEEE 31st International Conference on Software Engineering
Country/Territory	Canada
City	Vancouver
Period	16/05/09 → 24/05/09

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

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Using Quantitative Analysis to Implement Autonomic IT Systems
© 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.
Accepted author manuscript, 477 KB

Cite this

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title = "Using quantitative analysis to implement autonomic IT systems",

abstract = "The software underpinning today{\textquoteright}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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author = "Calinescu, {Radu C.} and Kwiatkowska, {Marta Z.}",

note = "{\textcopyright} 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.; 2009 IEEE 31st International Conference on Software Engineering ; Conference date: 16-05-2009 Through 24-05-2009",

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N2 - 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.

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.

KW - Markov processes

KW - Web services

KW - computer centres

KW - probability

KW - program diagnostics

KW - program verification

KW - software architecture

KW - software fault tolerance

KW - software maintenance

KW - Markov chain

KW - PRISM probabilistic model checker

KW - adaptive cluster availability management

KW - autonomic legacy IT system

KW - autonomic software architecture

KW - data centre

KW - disk drive

KW - dynamic power management

KW - quantitative analysis tool

KW - software framework

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M3 - Conference publication

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BT - IEEE 31st International Conference on Software Engineering, 2009. ICSE 2009

PB - IEEE

T2 - 2009 IEEE 31st International Conference on Software Engineering

Y2 - 16 May 2009 through 24 May 2009

ER -

Using quantitative analysis to implement autonomic IT systems

Abstract

Conference

Bibliographical note

Keywords

Access to Document

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Cite this