AI 中文总结
针对碳感知HPC作业无协调转移导致的时段拥塞问题，提出COAST机制，通过势博弈平衡碳节约与延迟，模拟显示碳排放减少15.4%，峰值负载降低23.8%。
AI 中文摘要
高性能计算（HPC）工作负载消耗大量电力，其碳排放量随电网碳强度随时间变化。然而，将碳感知HPC作业无协调地转移到低碳时段，可能导致推荐开始时间集中在同一时段，造成拥塞并削弱碳效益。本文提出COAST，一种拥塞感知的开始时间推荐机制，通过平衡碳节约与额外作业延迟及推荐开始时段内的拥塞来协调HPC作业。COAST将每个决策批次建模为精确势博弈，使最佳响应更新收敛到稳定的开始时间推荐。在假设每项作业能在其推荐时间开始的理想化实现模型下，我们使用公共HPC能源数据和历史电网碳强度轨迹进行轨迹驱动模拟。结果表明，COAST在额外平均开始延迟1.5小时的情况下，将估计碳排放量减少15.4%。与碳贪婪开始时间推荐相比，COAST将峰值时段负载降低23.8%，同时保留93%的可实现碳节约。这些结果量化了拥塞感知开始时间协调对灵活、碳感知HPC作业的潜在效益。
英文摘要
High-performance computing (HPC) workloads consume substantial amounts of electricity, and their carbon emissions vary over time with the carbon intensity of grid electricity. However, uncoordinated shifting of carbon-aware HPC jobs toward low-carbon periods can concentrate recommended start times in the same time slots, creating congestion and eroding the resulting carbon benefits. This paper proposes COAST, a congestion-aware start-time recommendation mechanism that coordinates HPC jobs by balancing carbon savings against additional job delay and congestion in recommended start-time slots. COAST formulates each decision batch as an exact potential game, enabling best-response updates to converge to stable start-time recommendations. Under an idealized realization model that assumes each job can start at its recommended time, we conduct trace-driven simulations using public HPC energy data and historical grid carbon-intensity traces. The results show that COAST reduces estimated carbon emissions by 15.4\% with 1.5 hours of additional average start delay. Compared with carbon-greedy start-time recommendations, COAST reduces peak time-slot load by 23.8\% while preserving 93\% of the achievable carbon savings. These results quantify the potential benefits of congestion-aware start-time coordination for flexible, carbon-aware HPC jobs.
