ABSTRACT
The critical need for clean and economical sources of energy is transforming data centers that are primarily energy consumers to also energy producers. We focus on minimizing the operating costs of next-generation data centers that can jointly optimize the energy supply from on-site generators and the power grid, and the energy demand from servers as well as power conditioning and cooling systems. We formulate the cost minimization problem and present an offline optimal algorithm. For "on-grid" data centers that use only the grid, we devise a deterministic online algorithm that achieves the best possible competitive ratio of 2αs, where αs is a normalized look-ahead window size. The competitive ratio of an online algorithm is defined as the maximum ratio (over all possible inputs) between the algorithm's cost (with no or limited look-ahead) and the offline optimal assuming complete future information. We remark that the results hold as long as the overall energy demand (including server, cooling, and power conditioning) is a convex and increasing function in the total number of active servers and also in the total server load. For "hybrid" data centers that have on-site power generation in addition to the grid, we develop an online algorithm that achieves a competitive ratio of at most Pmax(2--αs)/co+cm/L [1+2 Pmax--co/Pmax(1+αg], where αs and αg are normalized look-ahead window sizes, Pmax is the maximum grid power price, and L, co, and cm are parameters of an on-site generator.
Using extensive workload traces from Akamai with the corresponding grid power prices, we simulate our offline and online algorithms in a realistic setting. Our offline (resp., online) algorithm achieves a cost reduction of 25.8% (resp., 20.7%) for a hybrid data center and 12.3% (resp., 7.3%) for an on-grid data center. The cost reductions are quite significant and make a strong case for a joint optimization of energy supply and energy demand in a data center. A hybrid data center provides about 13% additional cost reduction over an on-grid data center representing the additional cost benefits that on-site power generation provides over using the grid alone.
- Akamai tech. http://www.akamai.com.Google Scholar
- Nationalgrid. https://www.nationalgridus.com/.Google Scholar
- Pacific gas and electric company. http://www.pge.com/nots/rates/tariffs/rateinfo.shtml.Google Scholar
- Tecogen. http://www.tecogen.com.Google Scholar
- The weather channal. http://www.weather.com/.Google Scholar
- Apple's onsite renewable energy, 2012. http://www.apple.com/environment/renewable-energy/.Google Scholar
- Distributed generation, 2012. http://www.bloomenergy.com/fuel-cell/distributed-generation/.Google Scholar
- C. Baldwin, K. Dale, and R. Dittrich. A study of the economic shutdown of generating units in daily dispatch. IEEE Trans. Power Apparatus and Systems, 1959.Google Scholar
- L. Barroso and U. Holzle. The case for energy-proportional computing. IEEE Computer, 2007. Google ScholarDigital Library
- A. Beloglazov, R. Buyya, Y. Lee, and A. Zomaya. A taxonomy and survey of energy-efficient data centers and cloud computing systems. Advances in Computers, 2011.Google Scholar
- A. Borbely and J. Kreider. Distributed generation: the power paradigm for the new millennium. CRC Press, 2001.Google ScholarCross Ref
- A. Borodin and R. El-Yaniv. Online computation and competitive analysis. Cambridge University Press, 1998. Google ScholarDigital Library
- J. Chase, D. Anderson, P. Thakar, A. Vahdat, and R. Doyle. Managing energy and server resources in hosting centers. In Proc. ACM SIGOPS, 2001. Google ScholarDigital Library
- A.J. Conejo, M.A. Plazas, R. Espinola, and A.B. Molina. Day-ahead electricity price forecasting using the wavelet transform and arima models. Power Systems, IEEE Transactions on, 2005.Google Scholar
- E. Dijkstra. A note on two problems in connexion with graphs. Numerische mathematik, 1959.Google Scholar
- R. Doyle, J. Chase, O. Asad, W. Jin, and A. Vahdat. Model-based resource provisioning in a web service utility. In Proc. USITS, 2003. Google ScholarDigital Library
- K. Fehrenbacher. ebay to build huge bloom energy fuel cell farm at data center. 2012. http://gigaom.com/cleantech/ebay-to-build-huge-bloom-energy-fuel-cell-farm-at-data-center/.Google Scholar
- K. Fehrenbacher. Is it time for more off-grid options for data centers?. 2012. http://gigaom.com/cleantech/is-it-time-for-more-off-grid-options-for-data-centers/.Google Scholar
- Daniel Gmach, Jerry Rolia, Ludmila Cherkasova, and Alfons Kemper. Workload analysis and demand prediction of enterprise data center applications. In Workload Characterization, 2007. IISWC 2007. IEEE 10th International Symposium on, 2007. Google ScholarDigital Library
- S. Kazarlis, A. Bakirtzis, and V. Petridis. A genetic algorithm solution to the unit commitment problem. IEEE Trans. Power Systems, 1996.Google Scholar
- J. Koomey. Growth in data center electricity use 2005 to 2010. Analytics Press, 2010.Google Scholar
- M. Lin, Z. Liu, A. Wierman, and L. Andrew. Online algorithms for geographical load balancing. In Proc. IEEE IGCC, 2012. Google ScholarDigital Library
- M. Lin, A. Wierman, L. Andrew, and E. Thereska. Dynamic right-sizing for power-proportional data centers. In Proc. IEEE INFOCOM, 2011.Google ScholarCross Ref
- Z. Liu, Y. Chen, C. Bash, A. Wierman, D. Gmach, Z. Wang, M. Marwah, and C. Hyser. Renewable and cooling aware workload management for sustainable data centers. In Proc. ACM SIGMETRICS, 2012. Google ScholarDigital Library
- J. Lowesohn. Apple's main data center to go fully renewable this year. 2012. http://news.cnet.com/8301--13579\textunderscore 3-57436553-37/apples-main-data-center-to-go-fully-renewable-this-year/.Google Scholar
- L. Lu, J. Tu, C. Chau, M. Chen, and X. Lin. Online energy generation scheduling for microgrids with intermittent energy sources and co-generation. In Proc. ACM SIGMETRICS, 2013. Google ScholarDigital Library
- T. Lu and Chen M. Simple and effective dynamic provisioning for power-proportional data centers. In Proc. IEEE CISS, 2012.Google ScholarCross Ref
- V. Mathew, R. Sitaraman, and P. Shenoy. Energy-aware load balancing in content delivery networks. In Proc. IEEE INFOCOM, 2012.Google ScholarCross Ref
- J. Muckstadt and R. Wilson. An application of mixed-integer programming duality to scheduling thermal generating systems. IEEE Trans. Power Apparatus and Systems, 1968.Google Scholar
- E. Nygren, R. Sitaraman, and J. Sun. The Akamai Network: A platform for high-performance Internet applications. 2010.Google Scholar
- N. Padhy. Unit commitment-a bibliographical survey. IEEE Trans. Power Systems, 2004.Google Scholar
- D. Palasamudram, R. Sitaraman, B. Urgaonkar, and R. Urgaonkar. Using batteries to reduce the power costs of internet-scale distributed networks. In Proc. ACM Symposium on Cloud Computing, 2012. Google ScholarDigital Library
- S. Pelley, D. Meisner, T. Wenisch, and J. VanGilder. Understanding and abstracting total data center power. In Workshop on Energy-Efficient Design, 2009.Google Scholar
- E. Pinheiro, R. Bianchini, E. Carrera, and T. Heath. Load balancing and unbalancing for power and performance in cluster-based systems. In Workshop on compilers and operating systems for low power, 2001.Google Scholar
- A. Qureshi, R. Weber, H. Balakrishnan, J. Guttag, and B. Maggs. Cutting the electric bill for internet-scale systems. In Proc. ACM SIGCOMM, 2009. Google ScholarDigital Library
- T. Shiina and J. Birge. Stochastic unit commitment problem. International Trans. Operational Research, 2004.Google Scholar
- M. Stadler, H. Aki, R. Lai, C. Marnay, and A. Siddiqui. Distributed energy resources on-site optimization for commercial buildings with electric and thermal storage technologies. Lawrence Berkeley National Laboratory, 2008.Google Scholar
- R. Stanojevic and R. Shorten. Distributed dynamic speed scaling. In Proc. IEEE INFOCOM, 2010. Google ScholarDigital Library
- J. Tu, L. Lu, M. Chen, and R. Sitaraman. Dynamic provisioning in next-generation data centers with on-site power production. Technical report, Department of Information Engineering, CUHK, 2013. http://arxiv.org/abs/1303.6775.Google Scholar
- H. Xu, C. Feng, and B. Li. Temperature aware workload management in geo-distributed datacenters. In Proc. ACM SIGMETRICS, extended abstract, 2013. Google ScholarDigital Library
Index Terms
- Dynamic provisioning in next-generation data centers with on-site power production
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