Multi-agent Reinforcement Learning Based Resource Allocation in End-Edge-Cloud Enabled Industrial Internet of Things

Abstract

The dynamic changes of task requirement and the time-varying distribution of resources in Industrial Internet of Things (IIoT) make a challenge for traditional static resource allocation methods to flexibly adapt to these changes. These can lead to increase latency and energy consumption, which result in low efficiency of resource allocation. In this paper, a resource allocation method based on multi-agent reinforcement learning (MARL) in end-edge-cloud enabled IIoT is proposed. The method builds an end-edge-cloud collaboration resource management model in IIoT scenarios, and constructs the optimization problem of minimizing task latency and energy consumption. Then, the optimization problem is further transformed into a multi-agent Markov decision process (MDP). Furthermore, the multi-agent deep deterministic policy gradient (MADDPG) is adopted to solve the formulated MDP problem. Finally, simulation results demonstrate that the proposed algorithm can significantly reduce the task latency cost by 27% and energy consumption cost by 15% compared with that of the baseline methods.