Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3604930.3605709acmconferencesArticle/Chapter ViewAbstractPublication PageshotcarbonConference Proceedingsconference-collections
research-article
Public Access

The War of the Efficiencies: Understanding the Tension between Carbon and Energy Optimization

Published: 02 August 2023 Publication History

Abstract

Major innovations in computing have been driven by scaling up computing infrastructure, while aggressively optimizing operating costs. The result is a network of worldwide datacenters that consume a large amount of energy, mostly in an energy-efficient manner. Since the electric grid powering these datacenters provided a simple and opaque abstraction of an unlimited and reliable power supply, the computing industry remained largely oblivious to the carbon intensity of the electricity it uses. Much like the rest of the society, it generally treated the carbon intensity of the electricity as constant, which was mostly true for a fossil fuel-driven grid. As a result, the cost-driven objective of increasing energy-efficiency --- by doing more work per unit of energy --- has generally been viewed as the most carbon-efficient approach. However, as the electric grid is increasingly powered by clean energy and is exposing its time-varying carbon intensity, the most energy-efficient operation is no longer necessarily the most carbon-efficient operation. There has been a recent focus on exploiting the flexibility of computing's workloads---along temporal, spatial, and resource dimensions---to reduce carbon emissions, which comes at the cost of either performance or energy efficiency. In this paper, we discuss the trade-offs between energy efficiency and carbon efficiency in exploiting computing's flexibility and show that blindly optimizing for energy efficiency is not always the right approach.

References

[1]
2014. The War of the Currents: AC vs. DC Power. https://www.energy.gov/articles/war-currents-ac-vs-dc-power.
[2]
2022. Annual Electric Power Industry Report. https://www.eia.gov/electricity/data/eia861/
[3]
2022. Global Trends in Internet Traffic, Data Centre Workloads and Data Centre Energy Use, 2010--2019. https://www.iea.org/data-and-statistics/charts/global-trends-in-internet-traffic-data-centre-workloads-and-data-centre-energy-use-2010-2019.
[4]
2022. Greenhouse Gas Protocol. https://ghgprotocol.org/.
[5]
2022. Share of Cumulative Power Capacity by Technology, 2010--2027. https://www.iea.org/data-and-statistics/charts/share-of-cumulative-power-capacity-by-technology-2010-2027.
[6]
2023. 24/7 by 2030: Realizing a Carbon-free Future. https://www.gstatic.com/gumdrop/sustainability/247-carbon-free-energy.pdf.
[7]
2023. Electricity Map. https://www.electricitymap.org/map.
[8]
2023. War of the Currents. https://en.wikipedia.org/wiki/War_of_the_currents.
[9]
Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Udit Gupta, Manoj Chakkaravarthy, David Brooks, and Carole-Jean Wu. 2023. Carbon Explorer: A Holistic Framework for Designing Carbon Aware Datacenters. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems.
[10]
Gene M Amdahl. 1967. Validity of the Single Processor Approach to Achieving Large Scale Computing Capabilities. In Proceedings of the Spring Joint Computer Conference.
[11]
Srini Bangalore, Arjita Bhan, Andrea Del Miglio, Pankaj Sachdeva, Vijay Sarma, Raman Sharma, and Bhargs Srivathsan. 2023. Investing in the Rising Data Center Economy. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/investing-in-the-rising-data-center-economy.
[12]
Noman Bashir, David Irwin, and Prashant Shenoy. 2023. On the Promise and Pitfalls of Optimizing Embodied Carbon. In Proceedings of the 2nd Workshop on Sustainable Computer Systems (HotCarbon).
[13]
Noman Bashir, David Irwin, Prashant Shenoy, and Abel Souza. 2022. Sustainable Computing - Without the Hot Air. In Proceedings of the First Workshop on Sustainable Computer Systems Design and Implementation (HotCarbon).
[14]
A. Chien. 2021. Driving the Cloud to True Zero Carbon. CACM 64, 2 (February 2021).
[15]
Jesse Dodge, Taylor Prewitt, Remi Tachet des Combes, Erika Odmark, Roy Schwartz, Emma Strubell, Alexandra Sasha Luccioni, Noah A. Smith, Nicole DeCario, and Will Buchanan. 2022. Measuring the Carbon Intensity of AI in Cloud Instances. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22).
[16]
Udit Gupta, Mariam Elgamal, Gage Hills, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, and Carole-Jean Wu. 2022. ACT: Designing Sustainable Computer Systems with an Architectural Carbon Modeling Tool. In ISCA.
[17]
Udit Gupta, Young Geun Kim, Sylvia Lee, Jordan Tse, Hsien-Hsin S Lee, Gu-Yeon Wei, David Brooks, and Carole-Jean Wu. 2021. Chasing Carbon: The Elusive Environmental Footprint of Computing. In 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA). IEEE.
[18]
Walid A Hanafy, Qianlin Liang, Noman Bashir, David Irwin, and Prashant Shenoy. 2023. CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency. arXiv preprint arXiv:2302.08681 (2023).
[19]
Jakub Krzywda, Ahmed Ali-Eldin, Trevor E Carlson, Per-Olov Östberg, and Erik Elmroth. 2018. Power-Performance Tradeoffs in Data Center Servers: DVFS, CPU Pinning, Horizontal, and Vertical scaling. Future Generation Computer Systems (2018).
[20]
Etienne Le Sueur and Gernot Heiser. 2010. Dynamic Voltage and Frequency Scaling: The Laws of Diminishing Returns. In Proceedings of the 2010 International Conference on Power Aware Computing and Systems.
[21]
Adam Lechowicz, Nicolas Christianson, Jinhang Zuo, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, and Prashant Shenoy. 2023. The Online Pause and Resume Problem: Optimal Algorithms and An Application to Carbon-Aware Load Shifting. arXiv preprint arXiv:2303.17551 (2023).
[22]
Mathieu Lemay, Kim-Khoa Nguyen, Bill St. Arnaud, and Mohamed Cheriet. 2012. Toward a Zero-Carbon Network: Converging Cloud Computing and Network Virtualization. IEEE Internet Computing (2012).
[23]
David Lo, Liqun Cheng, Rama Govindaraju, Parthasarathy Ranganathan, and Christos Kozyrakis. 2015. Heracles: Improving Resource Efficiency at Scale. In 2015 ACM/IEEE 42nd Annual International Symposium on Computer Architecture (ISCA).
[24]
Eric Masanet, Arman Shehabi, Nuoa Lei, Sarah Smith, and Jonathan Koomey. 2020. Recalibrating Global Data Center Energy-use Estimates. Science (2020).
[25]
David Patterson, Joseph Gonzalez, Urs Hölzle, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean. 2022. The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. Technical Report. Google Inc.
[26]
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean. 2021. Carbon Emissions and Large Neural Network Training. Technical Report. arXiv.
[27]
Hang Qi, Evan R. Sparks, and Ameet Talwalkar. 2017. Paleo: A Performance Model for Deep Neural Networks. In Proceedings of the International Conference on Learning Representations.
[28]
Ana Radovanović, Ross Koningstein, Ian Schneider, Bokan Chen, Alexandre Duarte, Binz Roy, Diyue Xiao, Maya Haridasan, Patrick Hung, Nick Care, Saurav Talukdar, Eric Mullen, Kendal Smith, MariEllen Cottman, and Walfredo Cirne. 2023. Carbon-Aware Computing for Datacenters. IEEE Transactions on Power Systems (2023).
[29]
Prateek Sharma, Tian Guo, Xin He, David Irwin, and Prashant Shenoy. 2016. Flint: Batch-Interactive Data-Intensive Processing for Transient Servers. In ACM European Conference on Computer Systems (EuroSys).
[30]
Zhiming Shen, Qin Jia, Gur-Eyal Sela, Ben Rainero, Weijia Song, Robbert van Renesse, and Hakim Weatherspoon. 2016. Follow the Sun through the Clouds: Application Migration for Geographically Shifting Workloads. In Proceedings of the Seventh ACM Symposium on Cloud Computing.
[31]
Abel Souza, Noman Bashir, Jorge Murillo, Walid Hanafy, Qianlin Liang, David Irwin, and Prashant Shenoy. 2023. Ecovisor: a Virtual Energy System for Carbon-Efficient Applications. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems.
[32]
Thanathorn Sukprasert, Abel Souza, Noman Bashir, David Irwin, and Prashant Shenoy. 2023. Quantifying the Benefits of Carbon-Aware Temporal and Spatial Workload Shifting in the Cloud. arXiv:2306.06502 [cs.DC]
[33]
Jennifer Switzer, Gabriel Marcano, Ryan Kastner, and Pat Pannuto. 2023. Junkyard Computing: Repurposing Discarded Smartphones to Minimize Carbon. In ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS).
[34]
Mark Weiser, Brent Welch, Alan Demers, and Scott Shenker. 1996. Scheduling for Reduced CPU Energy. Mobile Computing.
[35]
Philipp Wiesner, Ilja Behnke, Dominik Scheinert, Kordian Gontarska, and Lauritz Thamsen. 2021. Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud. In Proceedings of the 22nd International Middleware Conference (Middleware).
[36]
Timothy Wood, K.K. Ramakrishnan, Prashant Shenoy, and Jacobus Van der Merwe. 2011. CloudNet: Dynamic Pooling of Cloud Resources by Live WAN Migration of Virtual Machines. In International Conference on Virtual Execution Environments (VEE).
[37]
Chaojie Zhang, Alok Kumbhare, Ioannis Manousakis, Deli Zhang, Pulkit Misra, Rod Assis, Kyle Woolcock, Nithish Mahalingam, Brijesh Warrier, David Gauthier, Lalu Kunnath, Steve Solomon, Osvaldo Morales, Marcus Fontoura, and Ricardo Bianchini. 2021. Flex: High-Availability Datacenters With Zero Reserved Power. In Proceedings of the International Symposium on Computer Architecture (ISCA).

Cited By

View all
  • (2024)The War of the Efficiencies: Understanding the Tension between Carbon and Energy OptimizationACM SIGEnergy Energy Informatics Review10.1145/3698365.36983794:3(87-93)Online publication date: 1-Jul-2024
  • (2024)LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain DemandProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661942(27-45)Online publication date: 4-Jun-2024
  • (2024)On the Limitations of Carbon-Aware Temporal and Spatial Workload Shifting in the CloudProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3650079(924-941)Online publication date: 22-Apr-2024
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
HotCarbon '23: Proceedings of the 2nd Workshop on Sustainable Computer Systems
July 2023
145 pages
ISBN:9798400702426
DOI:10.1145/3604930
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].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 August 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. carbon efficiency
  2. energy efficiency
  3. sustainable computing

Qualifiers

  • Research-article

Funding Sources

Conference

HotCarbon '23
Sponsor:

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)203
  • Downloads (Last 6 weeks)35
Reflects downloads up to 10 Nov 2024

Other Metrics

Citations

Cited By

View all
  • (2024)The War of the Efficiencies: Understanding the Tension between Carbon and Energy OptimizationACM SIGEnergy Energy Informatics Review10.1145/3698365.36983794:3(87-93)Online publication date: 1-Jul-2024
  • (2024)LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain DemandProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661942(27-45)Online publication date: 4-Jun-2024
  • (2024)On the Limitations of Carbon-Aware Temporal and Spatial Workload Shifting in the CloudProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3650079(924-941)Online publication date: 22-Apr-2024
  • (2024)WattScopePerformance Evaluation10.1016/j.peva.2023.102369162:COnline publication date: 1-Feb-2024
  • (2023)CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-EfficiencyProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/36267887:3(1-28)Online publication date: 12-Dec-2023
  • (2023)The Online Pause and Resume Problem: Optimal Algorithms and An Application to Carbon-Aware Load ShiftingProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/36267767:3(1-32)Online publication date: 12-Dec-2023

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media