Metaheuristic optimization for long-term IaaS service composition

Mistry, S, Bouguettaya, A, Dong, H and Qin, K 2017, 'Metaheuristic optimization for long-term IaaS service composition', IEEE Transactions on Services Computing, vol. 11, no. 1, pp. 131-143.

Document type: Journal Article
Collection: Journal Articles

Title Metaheuristic optimization for long-term IaaS service composition
Author(s) Mistry, S
Bouguettaya, A
Dong, H
Qin, K
Year 2017
Journal name IEEE Transactions on Services Computing
Volume number 11
Issue number 1
Start page 131
End page 143
Total pages 14
Publisher IEEE
Abstract We propose a novel dynamic metaheuristic optimization approach to compose an optimal set of IaaS service requests to align with an IaaS provider's long-term economic expectation. This approach is designed for the context that the IaaS provisioning subjects to resource and QoS constraints. In addition, the IaaS service requests have the features of dynamic resource and QoS requirements and variable arrival times. A new economic model is proposed to evaluate the similarity between the provider's long-term economic expectation and a composition of service requests. The evaluation incorporates the factors of dynamic pricing and operation cost modeling of the service requests. An innovative hybrid genetic algorithm is proposed that incorporates the economic inter-dependency among the requests as a heuristic operator and performs repair operations in local solutions to meet the resource and QoS constraints. The proposed approach generates dynamic global solutions by updating the heuristic operator at regular intervals with the runtime behavior data of an existing service composition. Experimental results preliminarily prove the feasibility of the proposed approach.
Subject Interorganisational Information Systems and Web Services
Neural, Evolutionary and Fuzzy Computation
Pattern Recognition and Data Mining
Keyword(s) Quality of Service
Service Composition
Cloud Computing
Economic Model
Evolutionary Optimization
DOI - identifier 10.1109/TSC.2016.2542068
Copyright notice © 2016 IEEE
ISSN 1939-1374
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Citation counts: TR Web of Science Citation Count  Cited 9 times in Thomson Reuters Web of Science Article | Citations
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