Navigating the Energy Doldrums: Can We Exploit Energy-Price Volatility To Lower the Cost of Computing?
This work addresses energy cost management for HPC operators, but it is incremental as it builds on existing variable capacity concepts with a new modeling approach.
This paper tackles the problem of high energy costs in high-performance computing (HPC) by exploring variable capacity strategies that adjust compute resources based on fluctuating electricity prices, and it presents a model applied to real cluster data to estimate the impact on total cost of ownership.
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.