Cutting the electric bill for internet-scale systems

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback
Take a look at the new version of this page: [ beta version ]. Tell us what you think.

Cutting the electric bill for internet-scale systems

Full text

Pdf (697 KB)

Source

Applications, Technologies, Architectures, and Protocols for Computer Communication archive
Proceedings of the ACM SIGCOMM 2009 conference on Data communication table of contents

Barcelona, Spain

SESSION: Novel aspects to networking table of contents

Pages: 123-134

Year of Publication: 2009

ISBN:978-1-60558-594-9

Also published in ...

Authors

Asfandyar Qureshi	MIT, Cambridge, MA, USA
Rick Weber	Akamai Technologies, Cambridge, MA, USA
Hari Balakrishnan	MIT, Cambridge, MA, USA
John Guttag	MIT, Cambridge, MA, USA
Bruce Maggs	Carnegie Mellon University, Pittsburgh, PA, USA

Sponsors

ACM: Association for Computing Machinery
SIGCOMM: ACM Special Interest Group on Data Communication

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 50, Downloads (12 Months): 207, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1592568.1592584 What is a DOI?

ABSTRACT

Energy expenses are becoming an increasingly important fraction of data center operating costs. At the same time, the energy expense per unit of computation can vary significantly between two different locations. In this paper, we characterize the variation due to fluctuating electricity prices and argue that existing distributed systems should be able to exploit this variation for significant economic gains. Electricity prices exhibit both temporal and geographic variation, due to regional demand differences, transmission inefficiencies, and generation diversity. Starting with historical electricity prices, for twenty nine locations in the US, and network traffic data collected on Akamai's CDN, we use simulation to quantify the possible economic gains for a realistic workload. Our results imply that existing systems may be able to save millions of dollars a year in electricity costs, by being cognizant of locational computation cost differences.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	R. H. Katz, "Tech Titans Building Boom," IEEE Spectrum, February 2009.
	2	K. G. Brill, "The Invisible Crisis in the Data Center: The Economic Meltdown of Moore's Law," white paper, Uptime Institute, 2007.
	3	"Server and Data Center Energy Efficiency," Final Report to Congress, U.S. Environmental Protection Agency, 2007.
	4	Google Inc., "Efficient Computing: Data Centers." http://www.google.com/corporate/green/ datacenters/.
	5	Xiaobo Fan , Wolf-Dietrich Weber , Luiz Andre Barroso, Power provisioning for a warehouse-sized computer, Proceedings of the 34th annual international symposium on Computer architecture, June 09-13, 2007, San Diego, California, USA
	6	Luiz André Barroso , Urs Hölzle, The Case for Energy-Proportional Computing, Computer, v.40 n.12, p.33-37, December 2007 [doi>10.1109/MC.2007.443]
	7	David Meisner , Brian T. Gold , Thomas F. Wenisch, PowerNap: eliminating server idle power, Proceeding of the 14th international conference on Architectural support for programming languages and operating systems, March 07-11, 2009, Washington, DC, USA
	8	Gong Chen , Wenbo He , Jie Liu , Suman Nath , Leonidas Rigas , Lin Xiao , Feng Zhao, Energy-aware server provisioning and load dispatching for connection-intensive internet services, Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, p.337-350, April 16-18, 2008, San Francisco, California
	9	VMware DRS: Dynamic Scheduling of System Resources.
	10	N. Tolia, Z. Wang, M. Marwah, C. Bash, P. Ranganathan, and X. Zhu, "Delivering Energy Proportionality with Non Energy-Proportional Systems - Optimizing the Ensemble," in HotPower, 2008.
	11	Nikolai Joukov , Josef Sipek, GreenFS: making enterprise computers greener by protecting them better, Proceedings of the 3rd ACM SIGOPS/EuroSys European Conference on Computer Systems 2008, April 01-04, 2008, Glasgow, Scotland UK
	12	Randy Shoup, "Scalability Best Practices: Lessons from eBay."
	13	J. Markoff and S. Hansell, "Hiding in Plain Sight, Google Seeks an Expansion of Power," the New York Times, June 2006.
	14	Microsoft Environmental Sustainability group, "Q&A with Rob Bernard," Video.
	15	"61 Billion Searches Conducted Worldwide in August," Press Release, comScore Inc.
	16	United States Department of Energy, Official Statistics. http://www.eia.doe.gov.
	17	World Bank, "World Development Indicators Database."
	18	Platts, "Day-Ahead Market Prices," in Megawatt Daily, McGraw-Hill. 2006-2009.
	19	United States Federal Energy Regulatory Commission, Market Oversight. http://www.ferc.gov.
	20	Midwest ISO, "Market Concepts Study Guide," 2005.
	21	P. L. Joskow, "Markets for Power in the United States: an Interim Assessment," Aug. 2005.
	22	Severin Borenstein, "The Trouble With Electricity Markets: Understanding California's Restructuring Disaster," Journal of Economic Perspectives, 2005.
	23	L. Hadsell and H. A. Shawky, "Electricity Price Volatility and the Marginal Cost of Congestion: An Empirical Study of Peak Hours on the NYISO Market," The Energy Journal.
	24	U. Hölzle, "Powering a Google Search," Official Google Blog, Jan. 2009.
	25	J. Chabarek, J. Sommers, P. Barford, C. Estan, D. Tsiang, and S. Wright, "Power Awareness in Network Design and Routing," INFOCOM, 2008.
	26	"Commonwealth Edison." www.comed.com.
	27	K. C. Martin, P. L. Joskow, and A. D. Ellerman, "Time and Location Differentiated NOX Control in Competitive Electricity Markets Using Cap-and-Trade Mechanisms," April 2007.