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.

A MapReduce-based modified Grey Wolf optimizer for QoS-aware big service composition

Fiore U.
2019

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4780464
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