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.
- R. H. Katz, "Tech Titans Building Boom," IEEE Spectrum, February 2009. Google ScholarDigital Library
- K. G. Brill, "The Invisible Crisis in the Data Center: The Economic Meltdown of Moore's Law," white paper, Uptime Institute, 2007.Google Scholar
- "Server and Data Center Energy Efficiency," Final Report to Congress, U.S. Environmental Protection Agency, 2007.Google Scholar
- Google Inc., "Efficient Computing: Data Centers." http://www.google.com/corporate/green/ datacenters/.Google Scholar
- X. Fan, W.-D. Weber, and L. A. Barroso, "Power Provisioning for a Warehouse-sized Computer," in ACM International Symposium on Computer Architecture, 2007. Google ScholarDigital Library
- L. A. Barroso and U. Hölzle, "The Case for Energy Proportional Computing," IEEE Computer, 2007. Google ScholarDigital Library
- D. Meisner, B. T. Gold, and T. F. Wenisch, "PowerNap: Eliminating Server Idle power," in ACM ASPLOS, 2009. Google ScholarDigital Library
- G. Chen, W. He, J. Liu, S. Nath, L. Rigas, L. Xiao, and F. Zhao, "Energy-Aware Server Provisioning and Load dispatching for Connection-Intensive Internet Services," in NSDI, 2008. Google ScholarDigital Library
- VMware DRS: Dynamic Scheduling of System Resources.Google Scholar
- 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. Google ScholarDigital Library
- N. Joukov and J. Sipek, "GreenFS: Making Enterprise Computers Greener by Protecting Them Better," in ACM Eurosys, 2008. Google ScholarDigital Library
- Randy Shoup, "Scalability Best Practices: Lessons from eBay."Google Scholar
- J. Markoff and S. Hansell, "Hiding in Plain Sight, Google Seeks an Expansion of Power," the New York Times, June 2006.Google Scholar
- Microsoft Environmental Sustainability group, "Q&A with Rob Bernard," Video.Google Scholar
- "61 Billion Searches Conducted Worldwide in August," Press Release, comScore Inc.Google Scholar
- United States Department of Energy, Official Statistics. http://www.eia.doe.gov.Google Scholar
- World Bank, "World Development Indicators Database."Google Scholar
- Platts, "Day-Ahead Market Prices," in Megawatt Daily, McGraw-Hill. 2006-2009.Google Scholar
- United States Federal Energy Regulatory Commission, Market Oversight. http://www.ferc.gov.Google Scholar
- Midwest ISO, "Market Concepts Study Guide," 2005.Google Scholar
- P. L. Joskow, "Markets for Power in the United States: an Interim Assessment," Aug. 2005.Google Scholar
- Severin Borenstein, "The Trouble With Electricity Markets: Understanding California's Restructuring Disaster," Journal of Economic Perspectives, 2005.Google Scholar
- 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.Google Scholar
- U. Hölzle, "Powering a Google Search," Official Google Blog, Jan. 2009.Google Scholar
- J. Chabarek, J. Sommers, P. Barford, C. Estan, D. Tsiang, and S. Wright, "Power Awareness in Network Design and Routing," INFOCOM, 2008.Google Scholar
- "Commonwealth Edison." www.comed.com.Google Scholar
- 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.Google Scholar
Index Terms
- Cutting the electric bill for internet-scale systems
Recommendations
Cutting the electric bill for internet-scale systems
SIGCOMM '09Energy 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 ...
Binary fireworks algorithm application for optimal schedule of electric vehicle reserve in traditional and restructured electricity markets
The multipurpose use of electric vehicles (EVs) makes it a special entity in the transportation sector and in the modern smart grid for use as a reserve service. This paper presents an application of binary fireworks algorithm for solving a multi-...
Hybrid energy storage systems and battery management for electric vehicles
DAC '13: Proceedings of the 50th Annual Design Automation ConferenceElectric vehicles (EV) are considered as a strong alternative of internal combustion engine vehicles expecting lower carbon emission. However, their actual benefits are not yet clearly verified while the energy efficiency can be improved in many ways. ...
Comments