A Modified Manta Ray Foraging Algorithm for Edge Server Placement in Mobile Edge Computing

Owing to the rapid growth of the Internet of Things (IoT) and mobile devices, big data has been generated, and a cloud computing system is required to manipulate it. The main issue in this system is the access delay (AD) when the IoT and mobile devices offload their workload on the servers and the workload balance among them. Mobile edge computing (MEC), which has a sufficient distribution of edge servers (ESs) and resources, has been developed to overcome this problem. The placement of ESs in MEC is an NP-hard problem and a multi-objective optimization (MOO) problem that aims to reduce the AD between mobile users and ES and balance the workload among these servers. In this paper, we propose a new hybrid natural-inspired algorithm by combining the manta ray foraging algorithm and the uniform crossover operator to find the optimal edge server placement (ESP) in MEC and minimize the AD between mobile users and the ES. Although the standard manta ray foraging optimization (MRFO) algorithm can balance diversification and intensification, it suffers from premature convergence. We invoke a uniform crossover operator in the standard MRFO algorithm to overcome this problem. In addition, the standard MRFO algorithm is designed to solve continuous optimization problems and is unsuitable for solving discrete problems. However, ESP is a discrete optimization problem. We modified the operators in the standard MRFO algorithm to solve the ESP problem, achieving an optimal solution in terms of minimizing the AD between BSs and ESs and balancing the workload among ESs. The proposed algorithm is then tested on the Shanghai Telecom dataset and compared with five other algorithms. The results show that the proposed algorithm achieves the least AD, and the workload balance reaches about 52% compared with the other algorithms.