High energy consumption remains a key challenge in high-performance computing (HPC) systems, which often feature hundreds or thousands of nodes drawing substantial power even in idle or standby modes. Although powering down unused nodes can improve energy efficiency, choosing the wrong time to do so can degrade quality of service by delaying job execution. Machine learning, in particular reinforcement learning (RL), has shown promise in determining optimal times to switch nodes on or off. In this study, we enhance the performance of a deep reinforcement learning (DRL) agent for HPC power management by integrating curriculum learning (CL), a training approach that introduces tasks with gradually increasing difficulty. Using the Batsim-py simulation framework, we compare the proposed CL-based agent to both a baseline DRL method (without CL) and the conventional fixed-time timeout strategy. Experimental results confirm that an easy-to-hard curriculum outperforms other training orders in terms of reducing wasted energy usage. The best agent achieves a 3.73% energy reduction over the baseline DRL method and a 4.66% improvement compared to the best timeout configuration (shutdown every 15 minutes of idle time). In addition, it reduces average job waiting time by 9.24% and maintains a higher job-filling rate, indicating more effective resource utilization. Sensitivity tests across various switch-on durations, power levels, and cluster sizes further reveal the agent's adaptability to changing system parameters without retraining. These findings demonstrate that curriculum learning can significantly improve DRL-based power management in HPC, balancing energy savings, quality of service, and robustness to diverse configurations.

翻译：高能耗仍然是高性能计算（HPC）系统面临的一个关键挑战，这类系统通常包含数百或数千个节点，即使在空闲或待机模式下也会消耗大量电力。虽然关闭未使用的节点可以提高能源效率，但选择错误的关闭时机可能因延迟作业执行而降低服务质量。机器学习，特别是强化学习（RL），在确定节点开关的最佳时机方面已显示出潜力。在本研究中，我们通过整合课程学习（CL）——一种引入难度逐步递增任务的训练方法——来提升用于HPC电源管理的深度强化学习（DRL）智能体的性能。利用Batsim-py仿真框架，我们将所提出的基于CL的智能体与基线DRL方法（无CL）以及传统的固定时间超时策略进行了比较。实验结果证实，在减少能源浪费方面，从易到难的课程设置优于其他训练顺序。最佳智能体相比基线DRL方法实现了3.73%的能耗降低，相比最佳超时配置（每空闲15分钟关机一次）提升了4.66%的能效。此外，它将平均作业等待时间减少了9.24%，并保持了更高的作业填充率，表明资源利用更为有效。针对不同启动时长、功率水平和集群规模的敏感性测试进一步揭示了该智能体无需重新训练即可适应变化的系统参数的能力。这些发现表明，课程学习能够显著改善HPC中基于DRL的电源管理，在节能、服务质量以及对多样化配置的鲁棒性之间实现良好平衡。