Abstract
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. In order 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.
Highlights
Mobile cloud computing has been achieving significant progression
The main function of the proposed hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA): Begin; 1: Set the initial parameters 2: Initialize the population with n frogs; 3: For each frog, i∈n compute fitnesses (i); 4: Rank the Frogs based on their fitness; 5: Begin while the determined termination condition is achieved 6: Sort the frogs in the descending order according to their fitnesses; 7: Distribute the frogs into m memeplexes; 8: In each complex specify the best frogs via their fitnesses; 9: Call two-point crossover procedure to crossbred the best and worst frogs in the memeplexes; 10: Call one-point mutation procedure to mutate the randomly selected frogs; 11: Shuffle the evolved frogs; 12: End while 13: Return an optimized frog
To analyze the results of the proposed algorithm, Culture Algorithm (CA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GAPSO algorithm are used in 3 different scenarios
Summary
Mobile cloud computing has been achieving significant progression. The rapid development of cloud computing, mobile devices, and Internet of Things (IoT) domains [1, 2] have managed to considerable growth in the number of near-feasible web services that have different Quality of Service (QoS) metrics. In spite of the restriction of power-saving and energy consumption problem in the mobile cloud providers, finding an optimized service composition mechanism with consuming low energy factors is a key challenge. We proposed a hybrid Shuffled Frog Leaping Algorithm (SFLA)[11] and Genetic Algorithm (GA)[12] (SFGA) to minimize the energy consumption of mobile cloud providers for composing required atomic services for achieving optimal QoS criteria. The main contributions of this research are as follows: 1) Proposing an energy-aware service composition mechanism to minimize the energy consumption of mobile cloud providers. 2) Presenting a hybrid SFGA algorithm for the proposed mobile cloud service composition mechanism. 3) Providing optimal solutions for the proposed service composition mechanism based on the maximum level of QoS factors.
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