Optimal Fitness Aware Cloud Service Composition using an Adaptive Genotypes Evolution based Genetic Algorithm

• Published in: Future generation computer systems Vol. 94; pp. 185 - 198
• Main Authors: Jatoth, Chandrashekar, Gangadharan, G.R., Buyya, Rajkumar
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2019
• Subjects: Adaptive Genotypes Evolution; Cloud service composition; Genetic algorithm; Quality of Service (QoS); Quality of Service (QoS); Cloud service composition; Genetic algorithm; Adaptive Genotypes Evolution
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2018.11.022

With the seamless proliferation of cloud services, it becomes challenging to select and compose cloud services that satisfy the requirements of users. A service may be connected with another service(s) for satisfying a workflow/function in a service composition. Further, the service assessment based on one or two QoS parameters is not accurate enough to achieve the desired optimality in a cloud service composition. Most of the existing methods in the literature consider either a single QoS parameter or two QoS parameters for QoS-aware composition and do not consider the balancing of QoS parameters and/or the connectivity constraints between two compositions. In this paper, we present an Optimal Fitness Aware Cloud Service Composition (OFASC) using an Adaptive Genotype Evolution based Genetic Algorithm (AGEGA) dealing with multiple QoS parameters and providing the solutions that satisfy the balancing QoS parameters and connectivity constraints of service composition. Experimental results show that our approach enhances the efficiency of cloud service composition by converging quickly and obtains better composition when compared to other approaches. •A novel service composition method using an evolution based genetic algorithm.•Describes service composition dealing with multiple QoS parameters.•Considers the connectivity constraints between service composition.