Premium
A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition
Author(s) -
Bhaskar Bhattu,
Jatoth Chandrashekar,
Gangadharan G.R.,
Fiore Ugo
Publication year - 2019
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.5351
Subject(s) - big data , computer science , quality of service , service composition , service (business) , composition (language) , web service , process (computing) , convergence (economics) , distributed computing , database , data mining , world wide web , computer network , linguistics , philosophy , economy , economics , economic growth , operating system
Summary Big services are the collection of interrelated web services across virtual and physical domains, integrating service oriented computing and big data. The rapid growth of Big services that offer similar functionality with varying QoS attributes makes the process of selection and composition of these big services as highly challenging and complex. In this paper, we develop an efficient QoS‐aware Big service composition approach by applying a MapReduce based Modified Grey Wolf Optimizer (MR‐MGWO) that explores more search space, especially in a multidimensional environment. Our approach ensures an optimal balance of exploration and exploitation that enhances the convergence rate and minimizes the computational time. The empirical analysis illustrates that the performance of MR‐MGWO is superior to other similar approaches for solving Big service composition.