Cutting the Electric Bill for Internet-Scale Systems

• Main Authors: Qureshi, Asfandyar (Contributor), Weber, Rick (Author), Balakrishnan, Hari (Contributor), Guttag, John V. (Contributor), Maggs, Bruce (Author)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Association for Computing Machinery / ACM Special Interest Group on Data Communications, 2011-05-04T19:04:49Z.
• Subjects: Article
• Online Access: Get fulltext

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.
Nokia Corporation
National Science Foundation (U.S.) (Grant CNF-0435382)