skip to main content
research-article

Parasol and GreenSwitch: managing datacenters powered by renewable energy

Published:16 March 2013Publication History
Skip Abstract Section

Abstract

Several companies have recently announced plans to build "green" datacenters, i.e. datacenters partially or completely powered by renewable energy. These datacenters will either generate their own renewable energy or draw it directly from an existing nearby plant. Besides reducing carbon footprints, renewable energy can potentially reduce energy costs, reduce peak power costs, or both. However, certain renewable fuels are intermittent, which requires approaches for tackling the energy supply variability. One approach is to use batteries and/or the electrical grid as a backup for the renewable energy. It may also be possible to adapt the workload to match the renewable energy supply. For highest benefits, green datacenter operators must intelligently manage their workloads and the sources of energy at their disposal.

In this paper, we first discuss the tradeoffs involved in building green datacenters today and in the future. Second, we present Parasol, a prototype green datacenter that we have built as a research platform. Parasol comprises a small container, a set of solar panels, a battery bank, and a grid-tie. Third, we describe GreenSwitch, our model-based approach for dynamically scheduling the workload and selecting the source of energy to use. Our real experiments with Parasol, GreenSwitch, and MapReduce workloads demonstrate that intelligent workload and energy source management can produce significant cost reductions. Our results also isolate the cost implications of peak power management, storing energy on the grid, and the ability to delay the MapReduce jobs. Finally, our results demonstrate that careful workload and energy source management can minimize the negative impact of electrical grid outages.

References

  1. AISO.net. Web Hosting as Nature Intended, 2012. http://www.aiso.net/.Google ScholarGoogle Scholar
  2. B. Aksanli et al. Utilizing Green Energy Prediction to Schedule Mixed Batch and Service Jobs in Data Centers. In HotPower, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. M. Arlitt et al. Towards the Design and Operation of Net-Zero Energy Data Centers. In ITherm, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  4. Y. Chen et al. The Case for Evaluating MapReduce Performance Using Workload Suites. In MASCOTS, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Y. Chen et al. Statistical Workload Injector for MapReduce, 2012.Google ScholarGoogle Scholar
  6. Data Center Knowledge. Apple Plans 20MW of Solar Power for iDataCenter, 2012. http://www.datacenterknowledge.com/archives/-2012/02/20/apple-plans-20mw-of-solar-power-for-idatacenter/.Google ScholarGoogle Scholar
  7. Data Center Knowledge. Data Centers Scale Up Their Solar Power, 2012. http://www.datacenterknowledge.com/archives/2012/05/14/-data-centers-scale-up-their-solarpower/.Google ScholarGoogle Scholar
  8. EcobusinessLinks. Green Hosting - Sustainable Solar & Wind Energy Web Hosting, 2012. http://www.ecobusinesslinks.com/-green webhosts/.Google ScholarGoogle Scholar
  9. EPFL. CloudSuite, 2012. http://parsa.epfl.ch/cloudsuite/cloudsuite.html.Google ScholarGoogle Scholar
  10. D. Gmach et al. Capacity Planning and Power Management to Exploit Sustainable Energy. In CNSM, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  11. I. Goiri et al. GreenSlot: Scheduling Energy Consumption in Green Datacenters. In Supercomputing, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. I. Goiri et al. GreenHadoop: Leveraging Green Energy in Data-Processing Frameworks. In EuroSys, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. S. Govindan et al. Leveraging Stored Energy for Handling Power Emergencies in Aggressively Provisioned Datacenters. In ASPLOS, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. S. Govindan, A. Sivasubramanian, and B. Urgaonkar. Benefits and Limitations of Tapping into Stored Energy For Datacenters. In ISCA, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Green House Data. An Economically Responsible Data Center, 2012. http://www.greenhousedata.com/.Google ScholarGoogle Scholar
  16. Gurobi Optimization, Inc. Gurobi Optimizer 5.0, 2012. http://www.gurobi.com.Google ScholarGoogle Scholar
  17. J. Hamilton. I Love Solar Power But..., 2012. http://perspectives.-mvdirona.com/2012/03/17/ILoveSolarPowerBut.aspx.Google ScholarGoogle Scholar
  18. P. Hearps and D. McConnell. Renewable Energy Technology Cost Review. Technical report, Melbourne Energy Institute, 2011.Google ScholarGoogle Scholar
  19. IBM. Portable Modular Data Center, 2012. http://www-935.ibm.com/services/us/igs/flexible-design.html.Google ScholarGoogle Scholar
  20. International Electrotechnical Commission. Efficient Electrical Transmission and Distribution. Technical report, 2007.Google ScholarGoogle Scholar
  21. International Energy Agency. Technology Roadmap -- Solar Photovoltaic Energy. Technical report, 2010.Google ScholarGoogle Scholar
  22. V. Kontorinis et al. Managing Distributed UPS Energy for Effective Power Capping in Data Centers. In ISCA, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. J. Koomey. Growth in Data Center Electricity Use 2005 to 2010, 2011. Analytic Press.Google ScholarGoogle Scholar
  24. A. Krioukov et al. Integrating Renewable Energy Using Data Analytics Systems: Challenges and Opportunities. Bulletin of the IEEE Computer Society Technical Committee, 2011.Google ScholarGoogle Scholar
  25. A. Krioukov et al. Design and Evaluation of an Energy Agile Computing Cluster. Technical Report EECS-2012--13, UC Berkeley, 2012.Google ScholarGoogle Scholar
  26. K. Le et al. Cost- And Energy-Aware Load Distribution Across Data Centers. In HotPower, 2009.Google ScholarGoogle Scholar
  27. J. Leverich and C. Kozyrakis. On the Energy (In)efficiency of Hadoop Clusters. In HotPower, 2009.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. C. Li, A. Qouneh, and T. Li. iSwitch: Coordinating and Optimizing Renewable Energy Powered Server Clusters. In ISCA, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Z. Liu et al. Renewable and Cooling Aware Workload Management for Sustainable Data Centers. In SIGMETRICS, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. M. Love. Land Area and Storage Requirements for Wind and Solar Generation to Meet the US Hourly Electrical Demand. Master's thesis, Department of Mechanical Engineering, University of Victoria, 2003.Google ScholarGoogle Scholar
  31. J. Mankoff, R. Kravets, and E. Blevis. Some Computer Science Issues in Creating a Sustainable World. IEEE Computer, 41(8), 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. National Renewable Energy Laboratory. Wind Farm Area Calculator, 2012. http://www.nrel.gov/analysis/power databook/calc wind.php.Google ScholarGoogle Scholar
  33. PVOutput.org. PV Outputs, 2012. http://www.pvoutput.org.Google ScholarGoogle Scholar
  34. C. Ren et al. Carbon-Aware Energy Capacity Planning for Datacenters. In MASCOTS, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. F. D. Sacerdoti et al. Wide Area Cluster Monitoring with Ganglia. In Cluster, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  36. N. Sharma et al. Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems. In SECON, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  37. N. Sharma et al. Blink: Managing Server Clusters on Intermittent Power. In ASPLOS, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. SolarBuzz. Solar market research and analysis, 2011. http://www.solarbuzz.com/facts-and-figures.Google ScholarGoogle Scholar
  39. C. Stewart and K. Shen. Some Joules Are More Precious Than Others: Managing Renewable Energy in the Datacenter. In HotPower, 2009.Google ScholarGoogle Scholar
  40. R. Urgaonkar et al. Optimal Power Cost Management Using Stored Energy in Data Centers. In SIGMETRICS, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. US Department of Energy. 2010 Solar Technologies Market Report. Technical report, 2011.Google ScholarGoogle Scholar
  42. US Department of Energy. 2010 Wind Technologies Market Report. Technical report, 2011.Google ScholarGoogle Scholar
  43. US Energy Information Administration. Electricity prices, 2012. http://www.eia.gov/.Google ScholarGoogle Scholar
  44. US Environmental Protection Agency. Report to Congress on Server and Data Center Energy Efficiency, 2007.Google ScholarGoogle Scholar
  45. D. Wang et al. Energy Storage in Datacenters: What, Where, and How much? In SIGMETRICS, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Parasol and GreenSwitch: managing datacenters powered by renewable energy

    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

    Full Access

    • Published in

      cover image ACM SIGPLAN Notices
      ACM SIGPLAN Notices  Volume 48, Issue 4
      ASPLOS '13
      April 2013
      540 pages
      ISSN:0362-1340
      EISSN:1558-1160
      DOI:10.1145/2499368
      Issue’s Table of Contents
      • cover image ACM Conferences
        ASPLOS '13: Proceedings of the eighteenth international conference on Architectural support for programming languages and operating systems
        March 2013
        574 pages
        ISBN:9781450318709
        DOI:10.1145/2451116

      Copyright © 2013 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 16 March 2013

      Check for updates

      Qualifiers

      • research-article

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader
    About Cookies On This Site

    We use cookies to ensure that we give you the best experience on our website.

    Learn more

    Got it!