xPUE: Extending Power Usage Effectiveness Metrics for Cloud Infrastructures
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912273438605312 |
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| author | Fieni, Guillaume Rouvoy, Romain Seinturier, Lionel |
| author_facet | Fieni, Guillaume Rouvoy, Romain Seinturier, Lionel |
| contents | The energy consumption analysis and optimization of data centers have been an increasingly popular topic over the past few years. It is widely recognized that several effective metrics exist to capture the efficiency of hardware and/or software hosted in these infrastructures. Unfortunately, choosing the corresponding metrics for specific infrastructure and assessing its efficiency over time is still considered an open problem. For this purpose, energy efficiency metrics, such as the Power Usage Effectiveness (PUE), assess the efficiency of the computing equipment of the infrastructure. However, this metric stops at the power supply of hosted servers and fails to offer a finer granularity to bring a deeper insight into the Power Usage Effectiveness of hardware and software running in cloud infrastructure.Therefore, we propose to leverage complementary PUE metrics, coined xPUE, to compute the energy efficiency of the computing continuum from hardware components, up to the running software layers. Our contribution aims to deliver realtime energy efficiency metrics from different perspectives for cloud infrastructure, hence helping cloud ecosystems-from cloud providers to their customers-to experiment and optimize the energy usage of cloud infrastructures at large. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07124 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | xPUE: Extending Power Usage Effectiveness Metrics for Cloud Infrastructures Fieni, Guillaume Rouvoy, Romain Seinturier, Lionel Hardware Architecture Distributed, Parallel, and Cluster Computing The energy consumption analysis and optimization of data centers have been an increasingly popular topic over the past few years. It is widely recognized that several effective metrics exist to capture the efficiency of hardware and/or software hosted in these infrastructures. Unfortunately, choosing the corresponding metrics for specific infrastructure and assessing its efficiency over time is still considered an open problem. For this purpose, energy efficiency metrics, such as the Power Usage Effectiveness (PUE), assess the efficiency of the computing equipment of the infrastructure. However, this metric stops at the power supply of hosted servers and fails to offer a finer granularity to bring a deeper insight into the Power Usage Effectiveness of hardware and software running in cloud infrastructure.Therefore, we propose to leverage complementary PUE metrics, coined xPUE, to compute the energy efficiency of the computing continuum from hardware components, up to the running software layers. Our contribution aims to deliver realtime energy efficiency metrics from different perspectives for cloud infrastructure, hence helping cloud ecosystems-from cloud providers to their customers-to experiment and optimize the energy usage of cloud infrastructures at large. |
| title | xPUE: Extending Power Usage Effectiveness Metrics for Cloud Infrastructures |
| topic | Hardware Architecture Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2503.07124 |