Kavanagh, R orcid.org/0000-0002-9357-2459 and Djemame, K orcid.org/0000-0001-5811-5263 (2019) Rapid and accurate energy models through calibration with IPMI and RAPL. Concurrency Computation Practice and Experience, 31 (13). e5124. ISSN 1532-0626
Abstract
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.
Metadata
Item Type: | Article |
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Copyright, Publisher and Additional Information: | © 2019 John Wiley & Sons, Ltd. This is the peer reviewed version of the following article: Kavanagh, R, Djemame, K. Rapid and accurate energy models through calibration with IPMI and RAPL. Concurrency Computat Pract Exper. 2019; 31:e51246, which has been published in final form at https://doi.org/10.1002/cpe.5124. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. |
Keywords: | calibration; energy; energy model; IPMI; power; RAPL |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EU - European Union 687584 |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Dec 2018 12:51 |
Last Modified: | 09 Jan 2020 01:38 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/cpe.5124 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:140145 |