Ant colony optimization applied to web service compositions in cloud computing. (January 2015)

• Record Type: Journal Article
• Authors: Yu, Qiang; Chen, Ling; Li, Bin
• Is Part Of: Computers & electrical engineering. Volume 41(2015)
• Page Start: 18
• Page End: 27
• Publication Date: 2015-01
• Subjects: Service composition -- Multi-cloud base -- Cloud combination -- Ant colony optimization; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2014.12.004
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: We show that the minimum request set used in existing methods cannot guarantee the optimal solution. We present a 3-level tree to represent the cloud bases, and transfer the problem into set covering. We present a greedy algorithm to select a cloud combination containing minimum clouds. We present an ant colony optimization based algorithm to select the optimal cloud combination. Experimental results show that our methods can more efficiently find the optimal cloud combination. Abstract: Rapid developments in cloud computing technology mean that many different web services are now published and available in cloud data centers. There has recently been an increasing amount of interest in web service composition, because it is important in practical applications. However, most traditional service composition methods can only find service composition sequences in a single cloud, and cannot consider a multi-cloud service base. It is challenging to efficiently find a composition across multiple clouds, because it involves service compositions and combinatorial optimization. In this paper, we present a greedy algorithm called Greedy-WSC and an ant colony optimization based algorithm called ACO-WSC, which attempt to select cloud combinations that are feasible and use the minimum number of clouds. Our experimental results show that the proposed ant colony optimization method can effectively and efficiently find cloud combinations with a minimal number of clouds.