Mobile edge computing(MEC)networks bring computation and storage resources closer to the network edge to meet the low-latency requirements of future 6G services.Efficient management and scheduling of multidimensional resources in MEC networks are key to enhancing user experience.To address the resource management decision-making problem in MEC networks,this study proposes a distributed resource management algorithm based on Lyapunov optimization.Specifically,task data queues and virtual energy queues are introduced to ensure task execution fairness and mitigate excessive congestion under high system loads.The objective function is constructed using Lyapunov optimization theory and solved with a deep deterministic policy gradient(DDPG)-based algorithm.Furthermore,considering that MEC servers have varying requirements for DDPG-based decision models,heterogeneous networks are deployed across different edge servers,creating a distributed multi-continuous variable decision model.Simulation results demonstrate the superior convergence and stability of the proposed distributed decision-making algorithm.
维普
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