Multi-Agent Collaboration for Workflow Task Offloading in End-Edge-Cloud Environments Using Deep Reinforcement Learning

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.