skip to main content
10.1145/2342356.2342398acmconferencesArticle/Chapter ViewAbstractPublication PagescommConference Proceedingsconference-collections
research-article
Free Access

It's not easy being green

Published:13 August 2012Publication History

ABSTRACT

Large-scale Internet applications, such as content distribution networks, are deployed across multiple datacenters and consume massive amounts of electricity. To provide uniformly low access latencies, these datacenters are geographically distributed and the deployment size at each location reflects the regional demand for the application. Consequently, an application's environmental impact can vary significantly depending on the geographical distribution of end-users, as electricity cost and carbon footprint per watt is location specific. In this paper, we describe FORTE: Flow Optimization based framework for request-Routing and Traffic Engineering. FORTE dynamically controls the fraction of user traffic directed to each datacenter in response to changes in both request workload and carbon footprint. It allows an operator to navigate the three-way tradeoff between access latency, carbon footprint, and electricity costs and to determine an optimal datacenter upgrade plan in response to increases in traffic load. We use FORTE to show that carbon taxes or credits are impractical in incentivizing carbon output reduction by providers of large-scale Internet applications. However, they can reduce carbon emissions by 10% without increasing the mean latency nor the electricity bill.

Skip Supplemental Material Section

Supplemental Material

sigcomm-v-03-itsnoteasybeinggreen.mp4

mp4

84.7 MB

References

  1. Light-duty automotive technology, carbon dioxide emissions, and fuel economy trends: 1975 through 2011. U.S. Environmental Protection Agency.Google ScholarGoogle Scholar
  2. Akamai. Akamai reports fourth quarter 2010 and full-year 2010 financial results, 2011. http://tiny.cc/afyu9, Last visited Jan. 2012.Google ScholarGoogle Scholar
  3. H. Amur, J. Cipar, V. Gupta, G. R. Ganger, M. A. Kozuch, and K. Schwan. Robust and flexible power-proportional storage. In SoCC, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. L. Barroso and U. Holzle. The case for energy-proportional computing. Computer, 40(12):33--37, dec. 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. California ISO. Renewables watch, 2011. http: //www.caiso.com/green/renewableswatch.html.Google ScholarGoogle Scholar
  6. J. Chang, J. Meza, P. Ranganathan, C. Bash, and A. Shah. Green server design: beyond operational energy to sustainability. In HotPower, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. A. R. Curtis, S. Keshav, and A. López-Ortiz. LEGUP: Using heterogeneity to reduce the cost of data center network upgrades. In CoNEXT, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. B. Dennis. Estimating a data center's electrical carbon footprint. Schneider Electric White Paper 66, 2011.Google ScholarGoogle Scholar
  9. J. Doyle, D. O'Mahony, and R. Shorten. Server selection for carbon emission control. In GreenNet, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. EPA. EPA report to congress on server and data center energy efficiency. Technical report, U.S. Environmental Protection Agency, 2007.Google ScholarGoogle Scholar
  11. A. Feldmann, A. Gladisch, M. Kind, C. Lange, G. Smaragdakis, and F.-J. Westphal. Energy trade-offs among content delivery architectures. In CTTE, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  12. I. Goiri, K. Le, J. Guitart, J. Torres, and R. Bianchini. Intelligent placement of datacenters for internet services. In ICDCS, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Google. Efficient computing-step 2: efficient datacenters. http://www.google.com/corporate/green/datacenters/step2.html.Google ScholarGoogle Scholar
  14. Google green. http://www.google.com/green/, Last visited Jan. 2012.Google ScholarGoogle Scholar
  15. Google's Data Center Efficiency. http://www.google.com/about/datacenters/inside/efficiency/power-usage.html, Last visited Jan. 2012.Google ScholarGoogle Scholar
  16. A. G. Greenberg, J. R. Hamilton, D. A. Maltz, and P. Patel. The cost of a cloud: research problems in data center networks. SIGCOMM CCR, 39(1):68--73, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. K. Guan, G. Atkinson, D. Kilper, and E. Gulsen. On the energy efficiency of content delivery architectures. In ICC, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  18. B. Heller, S. Seetharaman, P. Mahadevan, Y. Yiakoumis, P. Sharma, S. Banerjee, and N. McKeown. Elastictree: saving energy in data center networks. In NSDI, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. B. H. Joseph V. Spadaro, Lucille Langlois. Greenhouse gas emissions of electricity generation chains: Assessing the difference. IAEA Bulletin, 2000.Google ScholarGoogle Scholar
  20. G. Jourjon, T. Rakotoarivelo, and M. Ott. Models for an energy-efficient p2p delivery service. In PDP, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. J. Kanter. Europe considers new taxes to promote 'clean' energy, 2010. http://nyti.ms/xSlobE, Last visited Jan. 2012.Google ScholarGoogle Scholar
  22. J. Koomey. Growth in data center electricity use 2005 to 2010. Analytics Press, Aug. 2011.Google ScholarGoogle Scholar
  23. J. G. Koomey. Worldwide electricity used in data centers. Environmental Research Letters, 3(3):034008, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  24. A. Krioukov, P. Mohan, S. Alspaugh, L. Keys, D. Culler, and R. H. Katz. Napsac: design and implementation of a power-proportional web cluster. In GreenNet, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. C. Labovitz, S. Iekel-Johnson, D. McPherson, J. Oberheide, and F. Jahanian. Internet inter-domain traffic. In SIGCOMM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. K. Le, R. Bianchini, T. Nguyen, O. Bilgir, and M. Martonosi. Capping the brown energy consumption of internet services at low cost. In IGCC, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. U. Lee, I. Rimac, and V. Hilt. Greening the internet with content-centric networking. In e-Energy, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. M. Lin, A. Wierman, L. L. H. Andrew, and E. Thereska. Dynamic right-sizing for power-proportional data centers. In INFOCOM, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  29. Z. Liu, M. Lin, A. Wierman, S. H. Low, and L. L. Andrew. Greening geographical load balancing. In SIGMETRICS, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. V. Mathew, R. Sitaraman, and P. Shenoy. Energy-aware load balancing in content delivery networks. In INFOCOM, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  31. R. Miller. Report: Google Uses About 900,000 Servers. Data Center Knowledge, http://tiny.cc/gservers, 2011. Last visited Jan 2012.Google ScholarGoogle Scholar
  32. A. Mohsenian-Rad and A. Leon-Garcia. Coordination of cloud computing and smart power grids. In SmartGridComm, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  33. E. Nygren, R. K. Sitaraman, and J. Sun. The akamai network: a platform for high-performance internet applications. SIGOPS OSR, 44:2--19, August 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. J. Park. Designing a Very Efficient Data Center. Facebook.com, http://tiny.cc/fbservers, 2011. Last visited Jan 2012.Google ScholarGoogle Scholar
  35. A. Qureshi, R. Weber, H. Balakrishnan, J. Guttag, and B. Maggs. Cutting the electric bill for Internet-scale systems. In SIGCOMM, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. B. Raghavan and J. Ma. The Energy and Emergy of the Internet. In HotNets, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. L. Rao, X. Liu, L. Xie, and W. Liu. Minimizing electricity cost: optimization of distributed internet data centers in a multi-electricity-market environment. In INFOCOM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. J. Rath. Data center site selection. In Rath Consulting White Paper, 2007.Google ScholarGoogle Scholar
  39. E. Schurman and J. Brutlag. The user and business impact of server delays, additional bytes, and HTTP chunking in web search. Presentation at the O'Reilly Velocity Web Performance and Operations Conference, 2009.Google ScholarGoogle Scholar
  40. A. Sullivan. ENERGY STAR for Data Centers. Technical report, U.S. Environmental Protection Agency, 2009.Google ScholarGoogle Scholar
  41. U.S. Energy Information Administration. http://www.eia.gov, Last visited Jan. 2012.Google ScholarGoogle Scholar
  42. V. Valancius, N. Laoutaris, L. Massoulié, C. Diot, and P. Rodriguez. Greening the internet with nano data centers. In CoNEXT, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. E. Williams. Energy intensity of computer manufacturing: hybrid assessment combining process and economic input-output methods. Environ. Sci. Technol., 38(22), Nov 2004.Google ScholarGoogle Scholar

Index Terms

  1. It's not easy being green

    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
      SIGCOMM '12: Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
      August 2012
      474 pages
      ISBN:9781450314190
      DOI:10.1145/2342356

      Copyright © 2012 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 ACM 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: 13 August 2012

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate554of3,547submissions,16%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader