Multi-Objective Accelerated Particle Swarm Optimization With Dynamic Programing Technique for Resource Allocation in Mobile Edge Computing

Mobile edge computing (MEC) is a powerful new technology with the potential to transform and decentralize the way our cell phone networks currently work. The purpose of MEC is to process the intensive mobile applications in the available resources, which are embedded in the base station of the cell phone systems and closer to users (i.e., MEC support stations). We assumed that the telecommunications base station supports MEC, which provides edge computing with tiny latency. However, the problem of inevitable optimization emerges in terms of the quality of service (QoS) and user experience (QoE). Therefore, MEC services provide integrated services close to end-users to achieve QoS and QoE. This study examined how to jointly optimize resource allocation when offloading tasks from mobile devices (MD) to edge servers (ES) in MEC systems, thereby minimizing the computing time and service cost. The study’s main insight is that offloaded tasks can be delivered in a scheduled manner to the virtual machines (VMs) in the ES to minimize computing time, service cost, waste over the capability of the ES, and maximum associativity (
${{\mathcal A}}_{E,X}$
) of a task with an ES to maintain MD mobility. We present a dynamic task scheduling and load-balancing technique based on an integrated accelerated particle swarm optimization (APSO) algorithm with dynamic programming as a multi-objective. The proposed method was compared with the standard PSO, APSO, and PSO-GA algorithms using experimental simulations. The results show that the proposed method outperformed these algorithms, with a reduction in task makespan of 30% and an increase in resource utilization of 29% observed compared to PSO-GA. Additionally, the proposed method was associated with reducing service cost and waiting time compared to the other algorithms and improvements in the fitness function value.