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.

通过增加技术和人类生活中的移动设备，使用运行时和移动服务以及大量原子服务的组成变得越来越复杂。移动云组件提供了不同的服务，以将非功能属性表示为服务质量（QoS），这由一组标准应用。另一方面，根据移动节点的节能问题，移动云中能源异质性的增长是一个新兴的挑战。为了将移动云服务的组成作为NP-Hard问题，应该采用最优的能量感知方法来解决问题，从而采取有效的选择方法，从而扩展移动云组件的部署和互操作性。也，需要一种能源感知服务组合机制来为移动云组件保留高节能方案。本文采用一种能量感知机制，使用混合蛙跳算法和遗传算法（SFGA）来优化移动云服务的组成。实验结果表明，相对于一些现有算法，该机制以最小的能耗，响应时间和移动云组件的成本提高了服务组合的可行性。