Abstract
Heavy power consumers, such as cloud providers and data center operators, can significantly benefit from multi-timescale electricity markets by purchasing some of the needed electricity ahead of time at cheaper rates. However, the energy procurement strategy for data centers in multi-timescale markets becomes a challenging problem when real world dynamics, such as the spatial diversity of data centers and the uncertainty of renewable energy, IT workload, and electricity price, are taken into account. In this paper, we develop energy procurement algorithms for geo-distributed data centers that utilize multi-timescale markets to minimize the electricity procurement cost. We propose two algorithms. The first algorithm provides provably optimal cost minimization while the other achieves near-optimal cost at a much lower computational cost. We empirically evaluate our energy procurement algorithms using real-world traces of renewable energy, electricity prices, and the workload demand. Our empirical evaluations show that our proposed energy procurement algorithms save up to 44% of the total cost compared to traditional algorithms that do not use multi-timescale electricity markets or geographical load balancing.
- http://www.eia.gov/.Google Scholar
- L. M. Ausubel and P. Cramton. Using forward markets to improve electricity market design. Utilities Policy, 18(4):195--200, 2010.Google ScholarCross Ref
- H. Beheshti and A. Croll. Performance impact: How web speed affects online business KPIs. In Velocity Online Conference. O'Reilly, 2009.Google Scholar
- P. Cramton. Colombia's forward energy market. 2007.Google Scholar
- M. Ghamkhari, A. Wierman, and H. Mohsenian-Rad. Energy portfolio optimization of data centers. IEEE Transactions on Smart Grid, 2016.Google Scholar
- Google. Renewable energy. http://www.google.com/ about/datacenters/renewable/index.html, 2015. {Online; accessed 25-April-2015}.Google Scholar
- Y. Guo, Z. Ding, Y. Fang, and D. Wu. Cutting down electricity cost in internet data centers by using energy storage. In Proc. IEEE GLOBECOM, pages 1--5, 2011.Google Scholar
- H. J. Kushner and G. Yin. Stochastic Approximation and Recursive Algorithms and Applications. Springer, 2003.Google Scholar
- T. N. Le, J. Liang, Z. Liu, R. K. Sitaraman, J. Nair, and B. J. Choi. Optimal energy procurement for geo-distributed data centers in multi-timescale markets. https://goo.gl/7OUB9h, March 2017.Google Scholar
- J. Liddle. Amazon found every 100ms of latency cost them 1% in sales. The GigaSpaces, 27, 2008.Google Scholar
- Z. Liu, M. Lin, A. Wierman, S. H. Low, and L. L. Andrew. Greening geographical load balancing. In Proc. ACM SIGMETRICS, pages 233--244, 2011. Google ScholarDigital Library
- A. Qureshi, R. Weber, H. Balakrishnan, J. Guttag, and B. Maggs. Cutting the electric bill for internet-scale systems. In Proc. ACM SIGCOMM, volume 39, pages 123--134, 2009. Google ScholarDigital Library
- 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 Proc. IEEE INFOCOM, pages 1--9, 2010. Google ScholarDigital Library
- L. Rao, X. Liu, L. Xie, and Z. Pang. Hedging against uncertainty: A tale of internet data center operations under smart grid environment. Smart Grid, IEEE Transactions on, 2(3):555--563, 2011.Google Scholar
- J. Whitney and P. Delforge. Data center efficiency assessment. Issue paper on NRDC (The Natural Resource Defense Council), 2014.Google Scholar
- L. Yu, T. Jiang, Y. Cao, S. Yang, and Z. Wang. Risk management in internet data center operations under smart grid environment. In Proc. IEEE SmartGridComm, pages 384--388, 2012.Google Scholar
- W. Zheng, K. Ma, and X. Wang. Exploiting thermal energy storage to reduce data center capital and operating expenses. In Proc. IEEE HPCA, pages 132--141, 2014.Google ScholarCross Ref
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