Efficient Task Offloading and Resource Allocation in MEC

Abstract

This paper proposes a novel optimization method for task offloading in Multi-Access Edge Computing (MEC) environments. The method combines Ant Colony Optimization (ACO) and Genetic Algorithms (GA) to minimize total execution latency. ACO explores the solution space for potential optimal solutions, while GA refines these solutions through evolutionary processes. Simulation experiments validate the effectiveness of this approach, showing significant reductions in overall execution latency compared to conventional single-algorithm methods. The paper also discusses key factors influencing task offloading strategies, providing practical insights for real-world deployments. The proposed hybrid ACO-GA strategy offers a high-efficiency and adaptable solution to the task allocation problem in MEC, enhancing the system's performance and quality.