Multi-Stage Heuristic Approach for Resource Allocation in Edge Computing: Enhancing Efficiency and Load Balancing

Resource allocation and task scheduling in edge computing environments are crucial for optimizing overall system performance. This paper introduces a multi-stage heuristic approach that combines local search, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to address the complexities of resource management in a three-layer architecture consisting of IoT devices, edge nodes, and cloud servers. The proposed method aims to minimize latency, reduce energy consumption, and maintain load balance by intelligently distributing computational tasks among the available nodes. The multi-objective optimization framework dynamically adapts to changes in workload and network conditions, thereby enhancing the efficiency of the system. Experimental results show that the proposed approach outperforms traditional methods in terms of reducing response time and energy usage while achieving a balanced load distribution across edge nodes, making it an effective solution for real-time and resource-intensive applications in edge computing environments.