Efficient Task Offloading Strategy for Energy-Constrained Edge Computing Environments: A Hybrid Optimization Approach

Abstract

Edge Computing (EC) has emerged as a pivotal paradigm, offering solutions to address the challenges posed by latency-sensitive applications and to enhance overall network performance. In EC environments, efficient task offloading is crucial for minimizing latency and energy consumption while maximizing resource utilization. In this paper, we propose a hybrid task offloading approach (HybridTO) integrating Grey Wolf Optimizer and Particle Swarm Optimization. Our approach aims to optimize energy consumption and fulfil latency constraints in EC environments by taking into account various factors such as capacity constraints, proximity constraints, and latency requirements. Leveraging the collaborative capabilities inherent in EC servers, HybridTO offers a comprehensive solution to the task offloading problem. Through extensive simulations, we evaluate the performance of HybridTO against baseline approaches, demonstrating its superiority regarding energy usage, offloading utility and response delay, especially under conditions of limited resources. These results underscore the effectiveness of HybridTO as a promising solution for energy-efficient task offloading in EC environments, offering valuable insights for further research and development in this field.

Keywords

• Edge computing
• task offloading
• energy efficiency
• optimization
• hybrid algorithms