ENERGY-EFFICIENT DEEP REINFORCEMENT LEARNING FOR INTELLIGENT TASK SCHEDULING IN EDGE COMPUTING ENVIRONMENTS

Abstract

Edge computing has emerged as an effective paradigm for processing latency-sensitive applications by bringing computational resources closer to end users. However, efficient task scheduling remains a significant challenge due to limited computational capacity, dynamic workloads, and varying network conditions. This paper proposes an Energy-Efficient Deep Reinforcement Learning (EE-DRL) framework that optimizes task scheduling while minimizing energy consumption and execution delay. The proposed framework employs a Deep Q-Network (DQN) to dynamically allocate computational tasks among heterogeneous edge nodes. Experimental analysis demonstrates that the proposed approach reduces average task completion time by 23%, lowers energy consumption by 19%, and improves resource utilization compared with traditional scheduling algorithms. The proposed framework provides an intelligent, adaptive, and scalable solution for future edge computing infrastructures.