DPU-Enhanced Multi-Agent Actor-Critic Algorithm for Cross-Domain Resource Scheduling in Computing Power Network

The distribution of computing resources in the Computing Power Network (CPN) is uneven, leading to an imbalance in resource supply and demand within domains, necessitating cross-domain resource scheduling. To address the cross-domain resource scheduling challenge in CPN, this paper presents an Improved Multi-Agent Actor-Critic (IMAAC) resource scheduling approach leveraging Data Processing Unit (DPU) offloading. Initially, we introduce a cross-domain resource scheduling architecture tailored for CPN by leveraging DPU offloading. Specifically, we delegate certain functionalities of the Multi-Agent Deep Reinforcement Learning (MADRL) Agent to DPUs, aiming to mitigate communication costs incurred during the generation of cross-domain scheduling decisions. Second, we introduce the parallel experience ensemble and multi-head attention mechanism in the Multi-Agent Actor-Critic (MAAC) framework to compress the state-space dimensionality of agent association across domains. Finally, we introduce the parallelized dual-policy network structure to mitigate training instability and convergence challenges within the actor and critic networks. Experimental results showcase that IMAAC achieves noteworthy reductions of 5.98%~13.56%, 23.54%~33.55%, and 41.17%~58.88% in total system delay, energy consumption, and the number of discarded tasks, respectively, compared to benchmark experiments.