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QoS-aware Big service composition using MapReduce based evolutionary algorithm with guided mutation
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DOI：10.1016/j.future.2017.07.042.png)
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Abstract
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Big services are the collection of interrelated services across virtual and physical domains for analyzing and processing big data. Big service composition is a strategy of aggregating these big services from various domains that addresses the requirements of a customer. Generally, a composite service is created from a repository of services where individual services are selected based on their optimal values of Quality of Service (QoS) attributes distinct to each service composition. However, the problem of producing a service composition with an optimal QoS value that satisfies the requirements of a customer is a complex and challenging issue, especially in a Big service environment. In this paper, we propose a novel MapReduce-based Evolutionary Algorithm with Guided Mutation that leads to an efficient composition of Big services with better performance and execution time. Further, the method includes a MapReduce-skyline operator that improves the quality of results and the process of convergence. By performing T-test and Wilcoxon signed rank test at 1% level of significance, we observed that our proposed method outperforms other methods. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Web service
Big data
Quality of Service (QoS)
MapReduce
Meta-heuristic algorithm
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