Multi-objective service composition model based on cost-effective optimization

The widespread application of cloud computing results in the exuberant growth of services with the same functionality. Quality of service (QoS) is mostly applied to represent nonfunctional properties of services, and has become an important basis for service selection. The object of most existing op...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Vol. 48; no. 3; pp. 651 - 669
Main Authors: Huo, Ying, Qiu, Peng, Zhai, Jiyou, Fan, Dajuan, Peng, Huanfeng
Format: Journal Article
Language:English
Published: New York Springer US 01.03.2018
Springer Nature B.V
Subjects:
Artificial Intelligence
Classification
Cloud computing
Composition effects
Computer Science
Computing costs
Fitness
Machines
Manufacturing
Mechanical Engineering
Multiple objective analysis
Optimization
Processes
Quality of service
Sorting algorithms
Swarm intelligence
User requirements
Artificial bee colony algorithm
Service composition
Multi-objective optimization
Quality of service
Cost-effective
ISSN:0924-669X, 1573-7497
Online Access:Get full text
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Summary:The widespread application of cloud computing results in the exuberant growth of services with the same functionality. Quality of service (QoS) is mostly applied to represent nonfunctional properties of services, and has become an important basis for service selection. The object of most existing optimization methods is to maximize the QoS, which restricts the diversity of users’ requirements. In this paper, instead of optimization for the single object, we take maximization of QoS and minimization of cost as two objects, and a novel multi-objective service composition model based on cost-effective optimization is designed according to the complicated QoS requirements of users. Furthermore, to solve this complex optimization problem, the Elite-guided Multi-objective Artificial Bee Colony (EMOABC) algorithm is proposed from the addition of fast nondominated sorting method, population selection strategy, elite-guided discrete solution generation strategy and multi-objective fitness calculation method into the original ABC algorithm. The experiments on two datasets demonstrate that EMOABC has an advantage both on the quality of solution and efficiency as compared to other algorithms. Therefore, the proposed method can be better applicable to the cloud services selection and composition.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-017-0996-y