Cutting the Electric Bill for Internet-Scale Systems
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en_US |
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Association for Computing Machinery / ACM Special Interest Group on Data Communications
2011
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| Online Access: | http://hdl.handle.net/1721.1/62585 https://orcid.org/0000-0003-0992-0906 https://orcid.org/0000-0002-1455-9652 |
| _version_ | 1811085642299342848 |
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| author | Qureshi, Asfandyar Weber, Rick Balakrishnan, Hari Guttag, John V. Maggs, Bruce |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Qureshi, Asfandyar Weber, Rick Balakrishnan, Hari Guttag, John V. Maggs, Bruce |
| author_sort | Qureshi, Asfandyar |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:12:55Z |
| format | Article |
| id | mit-1721.1/62585 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:12:55Z |
| publishDate | 2011 |
| publisher | Association for Computing Machinery / ACM Special Interest Group on Data Communications |
| record_format | dspace |
| spelling | mit-1721.1/625852022-10-01T13:48:17Z Cutting the Electric Bill for Internet-Scale Systems Qureshi, Asfandyar Weber, Rick Balakrishnan, Hari Guttag, John V. Maggs, Bruce Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Balakrishnan, Hari Qureshi, Asfandyar Balakrishnan, Hari Guttag, John V. 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) 2011-05-04T19:04:49Z 2011-05-04T19:04:49Z 2009-08 Article http://purl.org/eprint/type/ConferencePaper 978-1-60558-594-9 http://hdl.handle.net/1721.1/62585 Qureshi, Asfandyar et al. “Cutting the Electric Bill for Internet-scale Systems.” Proceedings of the ACM SIGCOMM 2009 Conference on Data Communication - SIGCOMM ’09. Barcelona, Spain, 2009. 123. Copyright c2009 ACM https://orcid.org/0000-0003-0992-0906 https://orcid.org/0000-0002-1455-9652 en_US http://dx.doi.org/10.1145/1594977.1592584 ACM SIGCOMM Conference on Data Communications. Proceedings Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Association for Computing Machinery / ACM Special Interest Group on Data Communications MIT web domain |
| spellingShingle | Qureshi, Asfandyar Weber, Rick Balakrishnan, Hari Guttag, John V. Maggs, Bruce Cutting the Electric Bill for Internet-Scale Systems |
| title | Cutting the Electric Bill for Internet-Scale Systems |
| title_full | Cutting the Electric Bill for Internet-Scale Systems |
| title_fullStr | Cutting the Electric Bill for Internet-Scale Systems |
| title_full_unstemmed | Cutting the Electric Bill for Internet-Scale Systems |
| title_short | Cutting the Electric Bill for Internet-Scale Systems |
| title_sort | cutting the electric bill for internet scale systems |
| url | http://hdl.handle.net/1721.1/62585 https://orcid.org/0000-0003-0992-0906 https://orcid.org/0000-0002-1455-9652 |
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