Decentralized Task Offloading in Edge Computing: An Offline-to-Online Reinforcement Learning Approach

Decentralized task offloading among cooperative edge nodes has been a promising solution to enhance resource utilization and improve users' Quality of Experience (QoE) in edge computing. However, current decentralized methods, such as heuristics and game theory-based methods, either optimize greedily or depend on rigid assumptions, failing to adapt to the dynamic edge environment. Existing DRL-based approaches train the model in a simulation and then apply it in practical systems. These methods may perform poorly because of the divergence between the practical system and the simulated environment. Other methods that train and deploy the model directly in real-world systems face a cold-start problem, which will reduce the users' QoE before the model converges. This paper proposes a novel offline-to-online DRL called (O2O-DRL). It uses the heuristic task logs to warm-start the DRL model offline. However, offline and online data have different distributions, so using offline methods for online fine-tuning will ruin the policy learned offline. To avoid this problem, we use on-policy DRL to fine-tune the model and prevent value overestimation. We evaluate O2O-DRL with other approaches in a simulation and a Kubernetes-based testbed. The performance results show that O2O-DRL outperforms other methods and solves the cold-start problem.