Online Decentralized Task Allocation Optimization for Edge Collaborative Networks

• Published in: 2022 IEEE Symposium on Computers and Communications (ISCC) pp. 1 - 6
• Main Authors: Zhang, Yaqiang, Li, Ruyang, Zhao, Yaqian, Li, Rengang, Li, Xuelei, Li, Tuo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 30-06-2022
• Subjects: Computers; Delays; Edge Collab-oration Networks; Federated learning; Multi-agent System; Online Decentralized Optimization; Real-time systems; Reinforcement learning; Resource management; Task Allocation; Training; Value Decomposition
• ISSN: 2642-7389
• DOI: 10.1109/ISCC55528.2022.9912855

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 per-formance 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 col-laborative 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.