An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment

Abstract

<p>By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy-saving problem in mobile nodes. To mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms. </p>

Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

“…Mohammed et al (2019) introduced a systematic and meta-analysis survey of Whale Optimization Algorithm modifying and hybridizing WOA algorithm with BAT algorithm in order to avoid local stagnation as well as increase the rate of convergence to achieve the global optimum solution. Ibrahim et al (2020) presented a hybrid meta-heuristic algorithm of Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA), an energy efficient service composition mechanism consuming minimum cost, response time and energy in a mobile cloud environment as compared to other algorithms. Muhammed et al (2020) proposed an Improved Fitness-Dependent Optimizer Algorithm IFDOA by first doing the randomization and then minimization of the weight fitness values using it in aperiodic antenna array designs.…”

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