🤖 AI Summary
Rising energy price volatility significantly increases the total cost of ownership (TCO) of high-performance computing (HPC) systems. Method: This paper proposes an energy budget management framework that integrates variable-capacity scheduling with a refined TCO model—specifically, the first to embed dynamic compute capacity adjustment into an HPC TCO model, enabling quantitative economic trade-off analysis among hardware utilization, energy-saving benefits, and idle-resource risk under time-varying electricity pricing. The approach leverages a parametric cost model calibrated with real operational data from a university-scale HPC cluster. Contribution/Results: Empirical simulation demonstrates that dynamic response to time-of-use electricity tariffs reduces energy expenditure by up to 18.7%; however, imposing a minimum load threshold is essential to mitigate resource idleness. The framework provides a practical, decision-support tool for jointly optimizing energy efficiency and economic performance in green-energy–integrated HPC infrastructures.
📝 Abstract
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.