A DRL-Based Joint Optimization Method for Service Placement and Task Offloading in Edge Computing

Abstract

Edge computing has gradually become a critical means of addressing the latency and bandwidth bottlenecks inherent in cloud computing. By deploying computing and storage resources at network edge nodes, edge computing is capable of providing low-latency and high-bandwidth services to Internet of Things (IoT) devices. To handle heterogeneous computing tasks at edge nodes, the corresponding services should be preplaced in advance, including image installation and data caching. In this study, we focus on the edge computing system that considers service placement. Taking into full account the latency and energy consumption incurred during task offloading and execution, as well as the additional cost introduced by service deployment, the joint optimization of service placement and task offloading decisions becomes exceedingly complex. Based on deep reinforcement learning (DRL), we propose a novel method that integrates double deep Q-network (DQN), dueling networks, and prioritized experience replay (PER) to make service placement and task offloading decisions, aiming to minimize the long-term weighted sum of task latency and energy consumption. Extensive simulation experiments demonstrate that our proposed method achieves stable convergence and outperforms other baseline methods.