Online Decentralized Task Allocation Optimization for Edge Collaborative Networks

In centralized task allocation strategies, real-time status information needs to be collected from distributed edge nodes. Therefore, the overloaded transmission on backbone network appears and leads to devastating decrease in the performance of centralized strategies. To address this issue, this paper proposes a multi-agent deep reinforcement learning based online decentralized task allocation mechanism, where each edge node makes task allocation decisions based on local network-state information. A centralized-training distributed-execution method is adopted to decrease data transmission load, and a value decomposition-based technique is applied at training stage for improving long-term performance of task allocation in edge collaborative networks. Extensive experiments are conducted, and evaluation results demonstrate that our mechanism outperforms other three baseline algorithms in reducing the long-term average system delay and improving request completion rate.