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QoS‐aware big service composition using distributed co‐evolutionary algorithm
Author(s) -
Dutta Avik,
Jatoth Chandrashekar,
Gangadharan G. R.,
Fiore Ugo
Publication year - 2021
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.6362
Subject(s) - scalability , computer science , evolutionary algorithm , quality of service , service composition , big data , distributed computing , service (business) , web service , distributed algorithm , composition (language) , data mining , database , artificial intelligence , computer network , world wide web , linguistics , philosophy , economy , economics
Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service‐aware big service composition methodology using a distributed co‐evolutionary algorithm. In our proposed model, we develop a distributed NSGA‐III for finding the optimal Pareto front and a distributed multi‐objective Jaya algorithm for enhancing the diversity of solutions. The distributed co‐evolutionary algorithm finds the near‐optimal solution in a fast and scalable way.