کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6873061 | 1440627 | 2018 | 39 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
QoS-aware Big service composition using MapReduce based evolutionary algorithm with guided mutation
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 86, September 2018, Pages 1008-1018
Journal: Future Generation Computer Systems - Volume 86, September 2018, Pages 1008-1018
نویسندگان
Chandrashekar Jatoth, G.R. Gangadharan, Ugo Fiore, Rajkumar Buyya,