PowerHeat: A non-intrusive approach for estimating the power consumption of bare metal water-cooled servers
Numerous cloud providers offer physical servers for rental in bare metal paradigm. This mode gives customers total control over hardware resources, but limits cloud providers’ visibility of their usage. Accurately measuring server energy consumption in this context represents a major challenge, as installing physical energy meters is both costly and complex. Existing energy models are generally based on system usage data, which is incompatible with the general privacy policies of bare-metal server contracts. To deal with these problems, it is imperative to develop new approaches for estimating the energy consumption of these servers. This paper presents an original non-intrusive method for estimating the energy consumption of a server cooled by direct-chip liquid-cooling, based on the coolant temperature and the processor temperature obtained via IPMI. Our approach is evaluated on an experiment carried out on 19 bare metal servers of a production infrastructure equipped with physical wattmeters.
Authors
- Manuel Agustí (ORCID: https://orcid.org/0000-0002-4271-6299)
- Benjamin Fichel
- Anne‐Cécile Orgerie (ORCID: https://orcid.org/0000-0003-0727-965X)
- Laurent Lefèvre (ORCID: https://orcid.org/0000-0002-5496-5882)
- Olivier Nicol
- Eddy Caron (ORCID: https://orcid.org/0000-0003-1529-3363)
Institutions
- Université Claude Bernard Lyon 1 (FR)
- École Normale Supérieure de Lyon (FR)
- Centre National de la Recherche Scientifique (FR)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Institut de Recherche en Informatique et Systèmes Aléatoires (FR)
- Centre Hospitalier de Roubaix (FR)
- Laboratoire de l'Informatique du Parallélisme (FR)
- Université de Rennes (FR)
Publication Details
- Published
- 2024-08-19
- DOI
- https://doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics62450.2024.00083
- Citations
- 1
- Primary Topic
- Green IT and Sustainability
- Type
- article
- Field-Weighted Citation Impact
- 0.21