skip to main content
10.1145/3472883.3487009acmconferencesArticle/Chapter ViewAbstractPublication PagesmodConference Proceedingsconference-collections
research-article

Enabling Sustainable Clouds: The Case for Virtualizing the Energy System

Published:01 November 2021Publication History

ABSTRACT

Cloud platforms' growing energy demand and carbon emissions are raising concern about their environmental sustainability. The current approach to enabling sustainable clouds focuses on improving energy-efficiency and purchasing carbon offsets. These approaches have limits: many cloud data centers already operate near peak efficiency, and carbon offsets cannot scale to near zero carbon where there is little carbon left to offset. Instead, enabling sustainable clouds will require applications to adapt to when and where unreliable low-carbon energy is available. Applications cannot do this today because their energy use and carbon emissions are not visible to them, as the energy system provides the rigid abstraction of a continuous, reliable energy supply. This vision paper instead advocates for a "carbon first" approach to cloud design that elevates carbon-efficiency to a firs--class metric. To do so, we argue that cloud platforms should virtualize the energy system by exposing visibility into, and software-defined control of, it to applications, enabling them to define their own abstractions for managing energy and carbon emissions based on their own requirements.

Skip Supplemental Material Section

Supplemental Material

Day2_7-2.mp4

mp4

595.5 MB

References

  1. 2018. OpenAI Blog, AI and Compute. https://openai.com/blog/ai-and-compute/.Google ScholarGoogle Scholar
  2. 2019. Reuters, Amazon Vows to be Carbon Neutral by 2040, buying 100,000 Electric Vans. https://www.reuters.com/article/us-amazon-environment/amazon-vows-to-be-carbon-neutral-by-2040-buying-100000-electric-vans-idUSKBN1W41ZV.Google ScholarGoogle Scholar
  3. 2020. Amazon EC2 Spot Instances. https://aws.amazon.com/ec2/spot/.Google ScholarGoogle Scholar
  4. 2020. Azure Spot Virtual Machines. https://azure.microsoft.com/en-us/pricing/spot/.Google ScholarGoogle Scholar
  5. 2020. Electricity Map. https://www.electricitymap.org/map.Google ScholarGoogle Scholar
  6. 2020. Google Preemptible Virtual Machines. https://cloud.google.com/preemptible-vms.Google ScholarGoogle Scholar
  7. 2021. Carbon free energy for Google Cloud regions. https://cloud.google.com/sustainability/region-carbon.Google ScholarGoogle Scholar
  8. 2021. Google Data Centers Efficiency. google.com/about/datacenters/efficiency/.Google ScholarGoogle Scholar
  9. 2021. Greenhouse Gas Protocol. https://ghgprotocol.org/.Google ScholarGoogle Scholar
  10. Nicola Acutt. 2018. Radius: Stories at the Edge, Achieving Carbon Neutrality. https://www.vmware.com/radius/achieving-carbon-neutrality/.Google ScholarGoogle Scholar
  11. A. Agarwal, J. Sun, S. Noghabi, S. Iyengar, A. Badam, R. Chandra, S. Seshan, and S. Kalyanaraman. 2021. Virtual Battery: Redesigning Cloud Computing for Renewable Energy. In HotNets.Google ScholarGoogle Scholar
  12. B. Alcott. [n.d.]. Jevons' Paradox. Ecological Economics 54, 1 ([n.d.]), 9--21.Google ScholarGoogle Scholar
  13. E. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. In ACM FAccT.Google ScholarGoogle Scholar
  14. Mark Bohr. 2007. A 30 Year Retrospective on Dennard's MOSFET Scaling Paper. IEEE Solid-State Circuits Society Newsletter 12, 1 (Winter 2007), 11--13.Google ScholarGoogle ScholarCross RefCross Ref
  15. Greg Bothun. 2020. Basics of Solar Energy. http://zebu.uoregon.edu/disted/ph162/l4.html.Google ScholarGoogle Scholar
  16. Jeffrey S Chase, Darrell C Anderson, Prachi N Thakar, Amin M Vahdat, and Ronald P Doyle. 2001. Managing Energy and Server Resources in Hosting Centers. ACM SIGOPS Operating Systems Review 35, 5, 103--116.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. A. Chien. 2021. Driving the Cloud to True Zero Carbon. CACM 64, 2 (February 2021).Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Alyssa Daniels. 2020. Environmental Leader, Google Signs PPA for 140MW from Solar Farm in Texas. https://www.environmentalleader.com/2020/09/google-candela-texas-solar-ppa/.Google ScholarGoogle Scholar
  19. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. Ieee, 248--255.Google ScholarGoogle ScholarCross RefCross Ref
  20. Dawson R. Engler, M. Frans Kaashoek, and James O'Toole. 1995. Exokernel: An Operating System Architecture for Application-Level Resource Management. In ACM Symposium on Operating System Principles (SOSP), Vol. 29. 251--266.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Darrell Etherington. 2020. TechCrunch, Google Claims Net Zero Carbon Footprint over its Entire Lifetime, Aims to only use Carbon-Free Energy by 2030. https://techcrunch.com/2020/09/14/google-claims-net-zero-carbon-footprint-over-its-entire-lifetime-aims-to-only-use-carbon-free-energy-by-2030/.Google ScholarGoogle Scholar
  22. S. Evans. 2020. CarbonBrief, Solar is now 'cheapest electricity in history', confirms IEA. https://www.carbonbrief.org/solar-is-now-cheapest-electricity-in-history-confirms-iea.Google ScholarGoogle Scholar
  23. Inigo Goiri, Ryan Beauchea, Kien Le, Thu D. Nguyen, Md. E. Haque, Jordi Guitart, Jordi Torres, and Ricardo Bianchini. 2011. GreenSlot: Scheduling Energy Consumption in Green Datacenters. In ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC). Seattle, Washington, 449--471.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Inigo Goiri, William Katsak, Kien Le, Thu D. Nguyen, and Ricardo Bianchini. 2013. Parasol and GreenSwitch: Managing Datacenters Powered by Renewable Energy. In ACM Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Vol. 48. Houston, Texas, 51--64.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. I. Goiri, T. Nguyen, and R. Bianchini. 2015. CoolAir: Temperature- and Variation-Aware Management for Free-Cooled Datacenters. In ASPLOS, Vol. 50. 253--265.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Synergy Research Group. 2020. Hyperscale Data Center Count Reaches 541 in Mid-2020; Another 176 in the Pipeline. Technical Report. Synergy Research Group, Reno, NV (United States). https://www.srgresearch.com/articles/hyperscale-data-center-count-reaches-541-mid-2020-another-176-pipelineGoogle ScholarGoogle Scholar
  27. Benjamin Hindman, Andy Konwinski, Matei Zaharia, Ali Ghodsi, Anthony Joseph, Randy Katz, Scott Shenker, and Ion Stoica. 2011. Mesos: A Platform for Fine-grained Resource Sharing in the Data Center. In USENIX Symposium on Networked Systems Design and Implementation (NSDI). Boston, Massachusetts, 295--308.Google ScholarGoogle Scholar
  28. Jeremy Hsu. 2019. How YouTube led to Google's cloud-gaming service: The tech that made YouTube work everywhere promises to do the same for games. IEEE Spectrum 56, 09 (2019), 9--10.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. C. Imes, H. Zhang, K. Zhao, and H. Hoffman. 2019. CoPPer: Soft Real-Time Application Performance Using Hardware Power Capping. In International Conference on Autonomic Computing (ICAC). 31--41.Google ScholarGoogle Scholar
  30. Rishikesh Jha, Stephen Lee, Srinivasan Iyengar, Mohammad H. Hajiesmaili, David Irwin, and Prashant Shenoy. 2020. Emission-aware Energy Storage Scheduling for a Greener Grid. In ACM International Conference on Future Energy Systems (e-Energy). 363--373.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Penny Jones. 2012. DataCenterDynamics, Apple Confirms Solar Farm at Maiden Data Center. https://www.datacenterdynamics.com/en/news/apple-conirms-solar-farm-at-maiden-data-center/.Google ScholarGoogle Scholar
  32. J. Koomey. 2011. Growth in Data Center Electricity Use 2005 to 2010. https://www.koomey.com/research.html.Google ScholarGoogle Scholar
  33. MLPerf. 2021. MLPerf™ v1.0 Inference Closed-Power ResNet-v1.5 server/offline (entries 1.0-{70,72,73,74}). https://mlcommons.org/en/inference-datacenter-10/Google ScholarGoogle Scholar
  34. E. Niiler. 2020. Wired, Do Carbon Offsets Really Work? It Depends on the Details. https://www.wired.com/story/do-carbon-offsets-really-work-it-depends-on-the-details/.Google ScholarGoogle Scholar
  35. Kevin O'Sullivan. 2020. The Irish Times, Facebook Commits to Net-Zero Carbon Emissions by 2030. https://www.irishtimes.com/news/environment/facebook-commits-to-net-zero-carbon-emissions-by-2030-1.4354701.Google ScholarGoogle Scholar
  36. Darshan S. Palasamudram, Ramesh K. Sitaraman, Bhuvan Urgaonkar, and Rahul Urgaonkar. 2012. Using Batteries to Reduce Power Costs of Internet-Scale Distributed Networks. In ACM Symposium on Cloud Computing (SoCC). 1--14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Srinivas Pandruvada. 2014. Running Average Power Limit. https://01.org/blogs/2014/running-average-power-limit-%E2%80%93-rapl.Google ScholarGoogle Scholar
  38. S. Pelley, D. Meisner, P. Zandevakili, T. Wenisch, and J. Underwood. 2010. Power Routing: Dynamic Power Provisioning in the Data Center. In ASPLOS, Vol. 38. 231--242.Google ScholarGoogle Scholar
  39. A. Qureshi, R. Weber, H. Balakrishnan, J. Guttag, and B. Maggs. 2009. Cutting the Electric Bill for Internet-Scale Systems. In SIGCOMM. 123--134.Google ScholarGoogle Scholar
  40. A. Radovanovic. 2020. Google Blog, Our data centers now work harder when the sun shines and wind blows. https://blog.google/inside-google/infrastructure/data-centers-work-harder-sun-shines-wind-blows/.Google ScholarGoogle Scholar
  41. Jerome H. Saltzer, David P. Reed, and David D. Clark. 1984. End-To-End Arguments in System Design. ACM Transactions on Computer Systems 2, 4 (November 1984), 277--288.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. 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). London, United Kingdom, 1--15.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Prateek Sharma, Stephen Lee, Tian Guo, David Irwin, and Prashant Shenoy. 2015. SpotCheck: Designing a Derivative Cloud on the Spot Market. In ACM European Conference on Computer Systems (EuroSys). Bordeaux, France, 1--15.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Arman Shehabi, Sarah Josephine Smith, Dale A. Sartor, Richard E. Brown, Magnus Herrlin, Jonathan G. Koomey, Eric R. Masanet, Nathanial Horner, Ines Lima Azevedo, and William Linter. 2016. United States Data Center Energy Usage Report. Technical Report LBNL-1005775. Lawrence Berkeley National Lab (LBL).Google ScholarGoogle Scholar
  45. Kai Shen, Arrvindh Shriraman, Sandhya Dwarkadas, Xiao Zhang, and Zhuan Chen. 2013. Power Containers: An OS Facility for Fine-grained Power and Energy Management on Multicore Servers. In ACM Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 65--76.Google ScholarGoogle Scholar
  46. Prashant Shenoy and Thomas Wenisch. 2015. NSF Workshop on Sustainable Data Centers. Technical Report. National Science Foundation.Google ScholarGoogle Scholar
  47. Rahul Singh, David Irwin, Prashant Shenoy, and K.K. Ramakrishnan. 2013. Yank: Enabling Green Data Centers to Pull the Plug. In USENIX Symposium on Networked Systems Design and Implementation (NSDI). 143--156.Google ScholarGoogle Scholar
  48. Brad Smith. 2020. Official Microsoft Blog, Microsoft will be Carbon Negative by 2030. https://blogs.microsoft.com/blog/2020/01/16/microsoft-will-be-carbon-negative-by-2030/.Google ScholarGoogle Scholar
  49. S. Sorrell. 2009. Jevons' Paradox Revisited: The Evidence for Backfire from Improved Energy Efficiency. 37, 4 (2009), 1456--1469.Google ScholarGoogle Scholar
  50. Rick Stevens, Valerie Taylor, Jeff Nichols, Arthur Barney Maccabe, Katherine Yelick, and David Brown. 2020. AI for Science. Technical Report. Argonne National Lab.(ANL), Argonne, IL (United States).Google ScholarGoogle Scholar
  51. Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2020. Energy and Policy Considerations for Modern Deep Learning Research. In AAAI Conference on Artificial Intelligence (AAAI). 13693--13696.Google ScholarGoogle Scholar
  52. Supreeth Subramanya, Tian Guo, Prateek Sharma, David Irwin, and Prashant Shenoy. 2015. SpotOn: A Batch Computing Service for the Spot Market. In Proceedings of the Sixth ACM Symposium on Cloud Computing (SoCC). Kohala Coast, Hawai'i, 1--13.Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. J. Switzer, R. McGuinness, P. Pannuto, G. Porter, A. Schulman, and B. Raghavan. 2021. TerraWatt: Sustaining Sustainable Computing of Containers in Containers. Technical Report. arXiv:2102.06614 https://arxiv.org/abs/2102.06614Google ScholarGoogle Scholar
  54. Domenico Talia. 2013. Clouds for scalable big data analytics. Computer 46, 5 (2013), 98--101.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Maud Texier. 2021. Google Cloud: A timely New Approach to Certifying Clean Energy. https://cloud.google.com/blog/topics/sustainability/t-eacs-offer-new-approach-to-certifying-clean-energy.Google ScholarGoogle Scholar
  56. R. Urgaonkar, B. Urgaonkar, M. Neely, and A. Sivasubramaniam. 2011. Optimal Power Cost Management Using Stored Energy in Data Centers. In SIGMETRICS. 221--232.Google ScholarGoogle Scholar
  57. M. Weiser, B. Welch, A. Demers, and S. Shenker. 1994. Scheduling for Reduced CPU Energy. In OSDI.Google ScholarGoogle Scholar
  58. J. Wilkes. 2020. Google Cluster-Usage Traces v3. Technical Report. Google Inc. Posted at https://github.com/google/cluster-data/blob/master/ClusterData2019.md.Google ScholarGoogle Scholar
  59. Ying Yan, Yanjie Gao, Yang Chen, Zhongxin Guo, Bole Chen, and Thomas Moscibroda. 2016. TR-Spark: Transient Computing for Big Data Analytics. In ACM Symposium on Cloud Computing (SoCC). Santa Clara, California, 484--496.Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Youngseok Yang, Geon-Woo Kim, Won Wook Song, Yunseong Lee, Andrew Chung, Zhengping Qian, Brian Cho, and Byung-Gon Chun. 2017. Pado: A Data Processing Engine for Harnessing Transient Resources in Datacenters. In ACM European Conference on Computer Systems (EuroSys). Belgrade, Serbia, 575--588.Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael Franklin, Scott Shenker, and Ion Stoica. 2012. Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. In USENIX Symposium on Networked System Design and Implementation (NSDI). 15--28.Google ScholarGoogle Scholar

Index Terms

  1. Enabling Sustainable Clouds: The Case for Virtualizing the Energy System

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        SoCC '21: Proceedings of the ACM Symposium on Cloud Computing
        November 2021
        685 pages
        ISBN:9781450386388
        DOI:10.1145/3472883

        Copyright © 2021 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: 1 November 2021

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited

        Acceptance Rates

        Overall Acceptance Rate169of722submissions,23%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader