Energy costs are a major factor in the total cost of ownership (TCO) for high-performance computing (HPC) systems. The rise of intermittent green energy sources and reduced reliance on fossil fuels have introduced volatility into electricity markets, complicating energy budgeting. This paper explores variable capacity as a strategy for managing HPC energy costs -- dynamically adjusting compute resources in response to fluctuating electricity prices. While this approach can lower energy expenses, it risks underutilizing costly hardware. To evaluate this trade-off, we present a simple model that helps operators estimate the TCO impact of variable capacity strategies using key system parameters. We apply this model to real data from a university HPC cluster and assess how different scenarios could affect the cost-effectiveness of this approach in the future.

翻译：能源成本是高性能计算（HPC）系统总拥有成本（TCO）的主要因素。间歇性绿色能源的兴起以及对化石燃料依赖的减少，导致电力市场波动加剧，使得能源预算规划复杂化。本文探讨了可变容量作为管理HPC能源成本的一种策略——根据波动的电价动态调整计算资源。虽然这种方法可以降低能源支出，但可能导致昂贵的硬件利用不足。为评估这一权衡，我们提出了一个简单模型，帮助运营者利用关键系统参数估算可变容量策略对总拥有成本的影响。我们将该模型应用于某大学HPC集群的实际数据，并评估不同场景在未来如何影响该方法的成本效益。