A Cooperative MADRL Approach for Joint Computing Offloading and Route Optimization in Cloud-Edge Networks
Future 6G networks will integrate communication and computing capabilities to support intelligent, delay-sensitive services. In heterogeneous cloud-edge environments, however, task offloading and routing decisions are strongly coupled, and dynamic workloads, limited computing resources, and constrained link capacity make efficient service provisioning challenging. Existing reinforcement learning-based offloading methods can improve decision efficiency, but many focus on simplified or single-domain settings and do not adequately account for backbone topology and bandwidth constraints. To address this problem, this paper studies joint task offloading and routing optimization in multi-domain cloud-edge networks, explicitly modeling network topology and link capacity. We propose a cooperative multi-agent deep reinforcement learning method that coordinates distributed edge agents through centralized training and decentralized execution. Routing optimization feedback is further incorporated to guide constraint-aware policy learning. Simulation results demonstrate that the proposed method reduces end-to-end latency, mitigates network congestion, and avoids link and node overload in cloud-edge networks.