Energy Efficient and Delay Aware Service Selection in Mobile Edge Computing

Mobile edge computing has been recently considered as a promising paradigm to satisfy the ever-increasing computation requirements from mobile applications. Although mobile edge computing may provide users with high-quality services by executing tasks at the network edge, due to the complexity of service requests and the limited service resources of edge servers, it is still a major challenge for mobile users how to select appropriate services so as to minimize the interaction delay of mobile users and the energy consumption of mobile devices. The service selection problem is a classic NP-hard problem. In this paper, we propose a hybrid algorithm (HGAPSO) that combines genetic algorithm and particle swarm optimization algorithm for service selection in mobile edge computing system to optimize the interaction delay and energy consumption. The performance of the algorithm is compared with that of other optimization algorithms through a simulation experiment. The results show that the proposed HGAPSO is superior to the current state-of-the-art algorithms applied to similar problems.