Dynamic Task Offloading Optimization in Mobile Edge Computing Systems with Time-Varying Workloads Using Improved Particle Swarm Optimization

Abstract

Mobile edge computing (MEC) enables offloading of compute-intensive and latency-sensitive tasks from resource-constrained mobile devices to servers at the network edge. This paper considers the dynamic optimization of task offloading in multi-user multi-server MEC systems with time-varying task workloads. The arrival times and computational demands of tasks are modeled as stochastic processes. The goal is to minimize the average task delay by optimal dynamic server selection over time. A particle swarm optimization (PSO) based algorithm is proposed that makes efficient offloading decisions in each time slot based on newly arrived tasks and pending workload across servers. The PSO-based policy is shown to outperform heuristics like genetic algorithms and simulated annealing in terms of adaptability to workload fluctuations and spikes. Experiments under varying task arrival rates demonstrate PSO’s capability to dynamically optimize time-averaged delay and energy costs through joint optimization of server selection and resource allocation. The proposed techniques provide a practical and efficient dynamic load balancing mechanism for real-time MEC systems with variable workloads.