Abstract:Deep reinforcement learning (DRL) has shown significant promise in computer cluster scheduling tasks. However, existing DRL-based cluster scheduling methods often lack sufficient generalization, hindering their effectiveness in highly dynamic and frequently changing cluster environments. To address this challenge, this study proposes an improved meta-learning optimization method for deep reinforcement learning cluster scheduling, termed MRLScheduler. The essence of this methodology lies in two improvements to meta-learning: First, a data generation module based on diffusion models generates diverse synthetic data during the initialization phase of meta-learning to expand and optimize multi-task datasets. Second, an experience replay module based on diffusion models leverages historical task data to generate synthetic experiences during cross-task training in meta-learning, enabling the reuse of historical experiences. Finally, the improved meta-learning is integrated into the deep reinforcement learning cluster scheduling algorithm to fine-tune the strategies of agents in highly dynamic and frequently changing cluster environments, thus improving their generalization ability. The experimental results indicate that MRLScheduler outperforms other baseline algorithms, effectively enhancing the generalization ability of deep reinforcement learning-based cluster scheduling algorithms.