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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.13155 |
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| _version_ | 1866910186597253120 |
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| author | Bader, Jonathan Irion, Julius Kappel, Jannis Witzke, Joel Fomin, Niklas Sherifi, Diellza Kao, Odej |
| author_facet | Bader, Jonathan Irion, Julius Kappel, Jannis Witzke, Joel Fomin, Niklas Sherifi, Diellza Kao, Odej |
| contents | The growing demand for data center capacity, driven by the growth of high-performance computing, cloud computing, and especially artificial intelligence, has led to a sharp increase in data center energy consumption. To improve energy efficiency, gaining process-level insights into energy consumption is essential. While node-level energy consumption data can be directly measured with hardware such as power meters, existing mechanisms for estimating per-process energy usage, such as Intel RAPL, are limited to specific hardware and provide only coarse-grained, domain-level measurements. Our proposed approach models per-process energy profiles by leveraging fine-grained process-level resource metrics collected via eBPF and perf, which are synchronized with node-level energy measurements obtained from an attached power distribution unit. By statistically learning the relationship between process-level resource usage and node-level energy consumption through a regression-based model, our approach enables more fine-grained per-process energy predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13155 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Process Energy Profiles from Node-Level Power Data Bader, Jonathan Irion, Julius Kappel, Jannis Witzke, Joel Fomin, Niklas Sherifi, Diellza Kao, Odej Distributed, Parallel, and Cluster Computing The growing demand for data center capacity, driven by the growth of high-performance computing, cloud computing, and especially artificial intelligence, has led to a sharp increase in data center energy consumption. To improve energy efficiency, gaining process-level insights into energy consumption is essential. While node-level energy consumption data can be directly measured with hardware such as power meters, existing mechanisms for estimating per-process energy usage, such as Intel RAPL, are limited to specific hardware and provide only coarse-grained, domain-level measurements. Our proposed approach models per-process energy profiles by leveraging fine-grained process-level resource metrics collected via eBPF and perf, which are synchronized with node-level energy measurements obtained from an attached power distribution unit. By statistically learning the relationship between process-level resource usage and node-level energy consumption through a regression-based model, our approach enables more fine-grained per-process energy predictions. |
| title | Learning Process Energy Profiles from Node-Level Power Data |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2511.13155 |