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On-line Access: 2011-03-09

Received: 2010-02-21

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.3 P.221-238


A self-optimizing QoS-aware service composition approach in a context sensitive environment

Author(s):  Yuan-hong Shen, Xiao-hu Yang

Affiliation(s):  School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   yhshen@zju.edu.cn, yangxh@zju.edu.cn

Key Words:  Service composition, Quality of service (QoS), Context, Repair genetic algorithm, Self-optimizing

Yuan-hong Shen, Xiao-hu Yang. A self-optimizing QoS-aware service composition approach in a context sensitive environment[J]. Journal of Zhejiang University Science C, 2011, 12(3): 221-238.

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%A Xiao-hu Yang
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%DOI 10.1631/jzus.C1000031

T1 - A self-optimizing QoS-aware service composition approach in a context sensitive environment
A1 - Yuan-hong Shen
A1 - Xiao-hu Yang
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1000031

QoS-aware service composition is aimed to maximize the global QoS of a composite service when selecting candidate services. In a context sensitive service execution environment in pervasive computing, the context information for service composition is not static: device, policy, and user constraints, and QoS requirements may change, new services may be deployed, old ones withdrawn, or existing ones change their QoS parameters. This results in the current service composition plan failing or its QoS degrading from the optimum. In this paper, a runtime self-optimizing service composition framework is proposed. An implementation of a prototype for this framework is presented, addressing the issues of reducing extra delay while increasing global QoS in service composition in a dynamic context environment. Three service re-plan algorithms are compared that can be used in dynamic context environment, i.e., minimal-conflict hill-climbing repair genetic algorithm (MCHC-repair GA), an improved penalty-based GA, and our multi-population conflicts sorted repair genetic algorithm (MP-CS-repair GA), as well as three kinds of service composition mechanisms—with backup, without backup, and our context-aware service re-selection mechanisms. The results show that our MP-CS-repair GA and context-aware service re-selection method can reduce more extra delay while acquiring a higher global QoS for the composite service in a context sensitive environment. This context-aware service re-selection mechanism also shows some adaptability to different context change frequencies and user requirements for reducing computation cost in the self-optimizing process.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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