Multi-Agent Collaboration for Workflow Task Offloading in End-Edge-Cloud Environments Using Deep Reinforcement Learning
Bohuai Xiao, Chujia Yu, Xing Chen, Zheyi Chen, Geyong Min · IEEE Transactions on Parallel and Distributed Systems · 2025
Computation offloading utilizes powerful cloud and edge resources to process workflow applications offloaded from Mobile Devices (MDs), effectively alleviating the resource constraints of MDs. In end-edge-cloud environments, workflow applications typically exhibit complex task dependencies. Meanwhile, parallel tasks from multi-MDs result in an expansive solution space for offloading decisions. Therefore, determining optimal offloading plans for highly dynamic and complex end-edge-cloud environments presents significant challenges. The existing studies on offloading tasks for multi-MD workflows often adopt centralized decision-making methods, which suffer from prolonged decision time, high computational overhead, and inability to identify suitable offloading plans in large-scale scenarios. To address these challenges, we propose a Multi-agent Collaborative method for Workflow Task offloading in end-edge-cloud environments with the Actor-Critic algorithm called MCWT-AC. First, each MD is modeled as an agent and independently makes offloading decisions based on local information. Next, each MD's workflow task offloading decision model is obtained through the Actor-Critic algorithm. At runtime, an effective workflow task offloading plan can be gradually developed through multi-agent collaboration. Extensive simulation results demonstrate that the MCWT-AC exhibits superior adaptability and scalability. Moreover, the MCWT-AC outperforms the state-of-art methods and can quickly achieve optimal/near-optimal performance.