A flexible QoS-aware Web service composition method by multi-objective optimization in cloud manufacturing. (September 2016)

• Record Type: Journal Article
• Authors: Chen, Fuzan; Dou, Runliang; Li, Minqiang; Wu, Harris
• Is Part Of: Computers & industrial engineering. Volume 99(2016)
• Page Start: 423
• Page End: 431
• Publication Date: 2016-09
• Subjects: Cloud manufacturing -- Web service composition -- Quality of service -- Multi-objective optimization -- Evolutionary algorithm; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2015.12.018
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: The QoS-aware service composition is optimized based on multi-objective optimization. Users gain decision flexibility with tradeoff between QoS performance and QoS variance from their expectation. An efficient EDMOGA is developed to achieve outstanding convergence and diversity of the Pareto frontier. Abstract: Cloud manufacturing, combining Web services via internet to a cooperative manufacturing system, has been an increasing popularity for global manufacturing. It will unlock the tremendous value in the massive amount of data being generated by the manufactories. The problem of QoS-aware Web service composition (QWSC), i.e., selecting appropriate service for each component of a service composition from a pool of functionally identical service to satisfy the users' end-to-end QoS constraints, is a core of the cloud manufacturing. A novel QWSC method by multi-objective optimization is proposed to help users to make a flexible decision. First of all, the problem of QWSC is formulated to a multi-objective optimization model where either QoS performance or QoS risk (variance comparing to the user's QoS requirement) is the individual optimization objective. And then, an efficient ε-dominance multi-objective evolutionary algorithm (EDMOEA) is developed to solve the presented model. Finally, experimental results verify the effectiveness and efficiency of the proposed method for the large-scale QWSC problem.