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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.07567 |
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| _version_ | 1866911559075233792 |
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| author | Arzt, Peter Wolf, Felix |
| author_facet | Arzt, Peter Wolf, Felix |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07567 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Navigating the Energy Doldrums: Can We Exploit Energy-Price Volatility To Lower the Cost of Computing? Arzt, Peter Wolf, Felix Distributed, Parallel, and Cluster Computing 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. |
| title | Navigating the Energy Doldrums: Can We Exploit Energy-Price Volatility To Lower the Cost of Computing? |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2509.07567 |