DR-DRL: Distributed Redundant Task Offloading Based on Reinforcement Learning

Edge computing enhances task reliability by employing redundant task executions across edge nodes. Conventional decentralized task offloading strategies, based on heuristics and game theory, either focus on optimization or are based on unrealistic assumptions. Moreover, existing Deep Reinforcement Learning (DRL) task offloading approaches underperform due to discrepancies between simulated environments and real systems, and overlook task redundancy, thus failing to meet reliability requirements. This paper proposes a DRL-based approach to offload distributed redundant tasks, namely DR-DRL (Decentralized and Redundant DRL), to solve the redundancy problem of distributed task offloading under edge computing. Experimental results show that DR-DRL has about 8% higher task success rate than other benchmark methods.