ESAVE: Estimating Server and Virtual Machine Energy
Authors: 
Priyavanshi Pathania, Rohit Mehra, Vibhu Saujanya Sharma, Vikrant Kaulgud, Sanjay Podder, Adam P. BurdenAuthors Info & Claims
Priyavanshi Pathania, Rohit Mehra, Vibhu Saujanya Sharma, Vikrant Kaulgud, Sanjay Podder, Adam P. BurdenAuthors Info & ClaimsASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
Article No.: 142, Pages 1 - 3
Abstract
Sustainable software engineering has received a lot of attention in recent times, as we witness an ever-growing slice of energy use, for example, at data centers, as software systems utilize the underlying infrastructure. Characterizing servers for their energy use accurately without being intrusive, is therefore important to make sustainable software deployment choices. In this paper, we introduce ESAVE which is a machine learning-based approach that leverages a small set of hardware attributes to characterize a server or virtual machine’s energy usage across different levels of utilization. This is based upon an extensive exploration of multiple ML approaches, with a focus on a minimal set of required attributes, while showcasing good accuracy. Early validations show that ESAVE has only around 12% average prediction error, despite being non-intrusive.
References
[1]
International Energy Agency. Accessed - 20/07/2022. Data Centres and Data Transmission Networks. https://www.iea.org/reports/data-centres-and-data-transmission-networks.
[2]
Lotfi Belkhir and Ahmed Elmeligi. 2018. Assessing ICT global emissions footprint: Trends to 2040 and recommendations. Journal of Cleaner Production 177 (2018), 448–463. https://doi.org/10.1016/j.jclepro.2017.12.239
[3]
United Nations Environment Programme: Copenhagen Climate Centre. Accessed - 20/07/2022. Greenhouse gas emissions in the ICT sector: trends and methodologies. https://c2e2.unepccc.org/wp-content/uploads/sites/3/2020/03/greenhouse-gas-emissions-in-the-ict-sector.pdf.
[4]
Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD ’16). Association for Computing Machinery, New York, NY, USA, 785–794. https://doi.org/10.1145/2939672.2939785
[5]
cloudcarbonfootprint.org. Accessed - 20/07/2022. Cloud Carbon Footrpint. https://www.cloudcarbonfootprint.org.
[6]
Arnaud de Myttenaere, Boris Golden, Bénédicte Le Grand, and Fabrice Rossi. 2016. Mean Absolute Percentage Error for regression models. Neurocomputing 192(2016), 38–48. https://doi.org/10.1016/j.neucom.2015.12.114 Advances in artificial neural networks, machine learning and computational intelligence.
[7]
Larry D. Gray, Anil Kumar, and Harry H. Li. 2008. Workload Characterization of the SPECpower_ssj2008 Benchmark. In Performance Evaluation: Metrics, Models and Benchmarks, Samuel Kounev, Ian Gorton, and Kai Sachs (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 262–282.
[8]
Chung-Hsing Hsu and Stephen W. Poole. 2011. Power signature analysis of the SPECpower_ssj2008 benchmark. In (IEEE ISPASS) IEEE International Symposium on Performance Analysis of Systems and Software. 227–236. https://doi.org/10.1109/ISPASS.2011.5762739
[9]
Eva Kern, Markus Dick, Stefan Naumann, and Tim Hiller. 2015. Impacts of software and its engineering on the carbon footprint of ICT. Environmental Impact Assessment Review 52 (2015), 53–61. https://doi.org/10.1016/j.eiar.2014.07.003 Information technology and renewable energy - Modelling, simulation, decision support and environmental assessment.
[10]
Patricia Lago. 2015. Challenges and Opportunities for Sustainable Software. In 2015 IEEE/ACM 5th International Workshop on Product Line Approaches in Software Engineering. 1–2. https://doi.org/10.1109/PLEASE.2015.8
[11]
linux.org. Accessed - 20/07/2022. TURBOSTAT. https://www.linux.org/docs/man8/turbostat.html.
[12]
Rohit Mehra, Vibhu Saujanya Sharma, Vikrant Kaulgud, Sanjay Podder, and Adam P. Burden. 2022. Towards a Green Quotient for Software Projects. In To appear in the Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Practice (Pittsburgh, Pennsylvania, USA) (ICSE ’22). ACM. https://doi.org/10.48550/arXiv.2204.12998
[13]
nature.com. Accessed - 20/07/2022. How to stop data centres from gobbling up the world’s electricity. https://www.nature.com/articles/d41586-018-06610-y.
[14]
Gustavo Pinto and Fernando Castor. 2017. Energy Efficiency: A New Concern for Application Software Developers. Commun. ACM 60, 12 (nov 2017), 68–75. https://doi.org/10.1145/3154384
[15]
GHG Protocol. 2017. ICT Sector Guidance built on the GHG Protocol Product Life Cycle Accounting and Reporting Standard. Global: GHG Protocol(2017).
[16]
pypi.org. Accessed - 20/07/2022. psutil. https://pypi.org/project/psutil/.
[17]
Jóakim von Kistowski, Klaus-Dieter Lange, Jeremy A. Arnold, Sanjay Sharma, Johann Pais, and Hansfried Block. 2018. Measuring and Benchmarking Power Consumption and Energy Efficiency. In Companion of the 2018 ACM/SPEC International Conference on Performance Engineering (Berlin, Germany) (ICPE ’18). Association for Computing Machinery, New York, NY, USA, 57–65. https://doi.org/10.1145/3185768.3185775
[18]
Xiao Zhang, Jianjun Lu, and Xiao Qin. 2013. BFEPM: Best Fit Energy Prediction Modeling Based on CPU Utilization. In 2013 IEEE Eighth International Conference on Networking, Architecture and Storage. 41–49. https://doi.org/10.1109/NAS.2013.12
Index Terms
- ESAVE: Estimating Server and Virtual Machine Energy
Index terms have been assigned to the content through auto-classification.
Recommendations
Revisiting Server Energy Proportionality
ICPP '13: Proceedings of the 2013 42nd International Conference on Parallel ProcessingServer energy proportionality refers to a proposed ideal that a server consumes energy proportional to its utilization level. Since its introduction in 2007, the ideal has been adopted by the server industry as a design goal to further optimize the ...
Estimating energy impact of software releases and deployment strategies: the KPMG case study
ESEM '17: Proceedings of the 11th ACM/IEEE International Symposium on Empirical Software Engineering and MeasurementBackground. Often motivated by optimization objectives, software products are characterized by different subsequent releases and deployed through different strategies. The impact of these two aspects of software on energy consumption has still to be ...
Energy-efficient strategy for virtual machine consolidation in cloud environment
AbstractAn important issue of energy efficiency in cloud environment is to perform more jobs while consuming less amount of power. Virtual machine consolidation remains the most deployed strategy to manage both performance and energy consumption. Most of ...
Comments
Information & Contributors
Information
Published In
October 2022
2006 pages
ISBN:9781450394758
DOI:10.1145/3551349
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 January 2023
Check for updates
Qualifiers
- Short-paper
- Research
- Refereed limited
Conference
ASE '22
ASE '22: 37th IEEE/ACM International Conference on Automated Software Engineering
October 10 - 14, 2022
MI, Rochester, USA
Acceptance Rates
Overall Acceptance Rate 82 of 337 submissions, 24%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 79Total Downloads
- Downloads (Last 12 months)31
- Downloads (Last 6 weeks)0
Reflects downloads up to 12 Aug 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format