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

TL;DR

This paper presents a hybrid meta-heuristic algorithm combining Shuffled Frog Leaping and Genetic Algorithms to optimize energy-efficient service composition in mobile cloud environments, reducing energy use and response time.

Contribution

It introduces a novel hybrid energy-aware service composition mechanism specifically designed for mobile clouds, addressing NP-hard optimization challenges.

Findings

Reduces energy consumption in mobile cloud service composition.

Improves response time and cost efficiency.

Enhances feasibility of service deployment in mobile clouds.

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. 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.