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Rapid and accurate energy models through calibration with IPMI and RAPL
Abstract
Summary: Energy consumption in Cloud and High Performance Computing platforms is a significant issue and affects aspects such as the cost of energy and the cooling of the data center. Host level monitoring and prediction provides the groundwork for improving energy efficiency through the placement of workloads. Monitoring must be fast and efficient without unnecessary overhead, to enable scalability. This precludes the use of Watt meters attached per host, requiring alternative approaches such as integrated measurements and models. IPMI and RAPL are subject to error and partial measurement, which may be mitigated. Models allow for prediction and more responsive measures of power consumption, but require calibrating. The causes of calibration error are discussed, along with mitigation strategies, without overly complicating the underlying model. An outcome is a Watt meter emulator that provides hosts level power measurement along with estimated power consumption for a given workload, with an average error of 0.20W.
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“A body of work has also looked at improving estimates of energy usage from RAPL by fitting a regression model to real energy usage patterns (Povoa et al., 2019; Kavanagh and Djemame, 2019; Ghosh et al., 2013; Song et al., 2013).”
From Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning (2020) · cites this paper as Kavanagh and Djemame, 2019
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“Errors highly depend on the specific chipset and even the workload”
From Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning (2020) · cites this paper as Kavanagh and Djemame (2019)
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“First, we rely on downstream hardware APIs which themselves have errors.”
From Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning (2020) · cites this paper as Kavanagh and Djemame (2019)
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Cited by
Sorted by citations
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020) · arXiv · doi:10.48550/arxiv.2002.05651 · Cited by 393
“34 A body of work has also looked at improving estimates of energy usage from RAPL by fitting a regression model to real energy usage patterns (Povoa et al., 2019; Kavanagh and Djemame, 2019; Ghosh et al., 2013; Song et al., 2013).”
Background
Metadata
- Authors
- Kavanagh, R., & Djemame, K.
- Affiliations
- University of Leeds (GB)
- Corresponding author
- Kavanagh, Richard
- Venue
- Concurrency and Computation: Practice and Experience
- Publisher
- Wiley
- Published
- 2019-01-09
- Volume
- 31
- Issue
- 13
- ISSN
- 1532-0626 (ISSN-L)
- 1532-0634
- OpenAlex
- W2924789150
- Cited by
- 23
- References
- 0
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 3 · 2023: 4 · 2022: 5 · 2021: 2 · 2020: 2 · 2019: 1
- FWCI
- 2.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Concurrency and Computation Practice and Experience (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Funders
- H2020 LEIT Information and Communication Technologies (687584)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- energy modeling, load forecasting, IPMI, RAPL, model calibration, calibration error, cloud computing energy efficiency, workload placement, data center cooling
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