ABSTRACT
Energy expenditure has become a significant fraction of data center operating costs. Recently, "geographical load balancing" has been suggested to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use.
This paper explores whether the geographical diversity of Internet-scale systems can additionally be used to provide environmental gains. Specifically, we explore whether geographical load balancing can encourage use of "green" renewable energy and reduce use of "brown" fossil fuel energy. We make two contributions. First, we derive two distributed algorithms for achieving optimal geographical load balancing. Second, we show that if electricity is dynamically priced in proportion to the instantaneous fraction of the total energy that is brown, then geographical load balancing provides significant reductions in brown energy use. However, the benefits depend strongly on the degree to which systems accept dynamic energy pricing and the form of pricing used.
Supplemental Material
- US Census Bureau, http://www.census.gov.Google Scholar
- Server and data center energy efficiency, Final Report to Congress, U.S. Environmental Protection Agency, 2007.Google Scholar
- V. K. Adhikari, S. Jain, and Z.-L. Zhang. YouTube traffic dynamics and its interplay with a tier-1 ISP: An ISP perspective. In ACM IMC, pages 431--443, 2010. Google ScholarDigital Library
- S. Albers. Energy-efficient algorithms. Comm. of the ACM, 53(5):86--96, 2010. Google ScholarDigital Library
- L. L. H. Andrew, M. Lin, and A. Wierman. Optimality, fairness and robustness in speed scaling designs. In Proc. ACM Sigmetrics, 2010. Google ScholarDigital Library
- A. Beloglazov, R. Buyya, Y. C. Lee, and A. Zomaya. A taxonomy and survey of energy-efficient data centers and cloud computing systems, Technical Report, 2010.Google Scholar
- D. P. Bertsekas. Nonlinear Programming. Athena Scientific, 1999.Google Scholar
- D. P. Bertsekas and J. N. Tsitsiklis. Parallel and Distributed Computation: Numerical Methods. Athena Scientific, 1989. Google ScholarDigital Library
- S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004. Google ScholarDigital Library
- S. Carley. State renewable energy electricity policies: An empirical evaluation of effectiveness. Energy Policy, 37(8):3071--3081, Aug 2009.Google ScholarCross Ref
- Y. Chen, A. Das, W. Qin, A. Sivasubramaniam, Q. Wang, and N. Gautam. Managing server energy and operational costs in hosting centers. In Proc. ACM Sigmetrics, 2005. Google ScholarDigital Library
- M. Conti and C. Nazionale. Load distribution among replicated web servers: A QoS-based approach. In Proc. ACM Worksh. Internet Server Performance, 1999.Google Scholar
- A. Croll and S. Power. How web speed affects online business KPIs. http://www.watchingwebsites.com, 2009.Google Scholar
- X. Fan, W.-D. Weber, and L. A. Barroso. Power provisioning for a warehouse-sized computer. In Proc. Int. Symp. Comp. Arch., 2007. Google ScholarDigital Library
- M. Fripp and R. H. Wiser. Effects of temporal wind patterns on the value of wind-generated electricity in california and the northwest. IEEE Trans. Power Systems, 23(2):477--485, May 2008.Google ScholarCross Ref
- A. Gandhi, M. Harchol-Balter, R. Das, and C. Lefurgy. Optimal power allocation in server farms. In Proc. ACM Sigmetrics, 2009. Google ScholarDigital Library
- http://www.datacenterknowledge.com, 2008.Google Scholar
- http://www.eia.doe.gov.Google Scholar
- S. Irani and K. R. Pruhs. Algorithmic problems in power management. SIGACT News, 36(2):63--76, 2005. Google ScholarDigital Library
- T. A. Kattakayam, S. Khan, and K. Srinivasan. Diurnal and environmental characterization of solar photovoltaic panels using a PC-AT add on plug in card. Solar Energy Materials and Solar Cells, 44(1):25--36, Oct 1996.Google ScholarCross Ref
- S. Kaxiras and M. Martonosi. Computer Architecture Techniques for Power-Efficiency. Morgan & Claypool, 2008. Google ScholarDigital Library
- R. Krishnan, H. V. Madhyastha, S. Srinivasan, S. Jain, A. Krishnamurthy, T. Anderson, and J. Gao. Moving beyond end-to-end path information to optimize CDN performance. In Proc. ACM Sigcomm, 2009. Google ScholarDigital Library
- K. Le, R. Bianchini, T. D. Nguyen, O. Bilgir, and M. Martonosi. Capping the brown energy consumption of internet services at low cost. In Proc. IGCC, 2010. Google ScholarDigital Library
- M. Lin, A. Wierman, L. L. H. Andrew, and E. Thereska. Dynamic right-sizing for power-proportional data centers. In Proc. IEEE INFOCOM, 2011.Google ScholarCross Ref
- Z. M. Mao, C. D. Cranor, F. Bouglis, M. Rabinovich, O. Spatscheck, and J. Wang. A precise and efficient evaluation of the proximity between web clients and their local DNS servers. In USENIX, pages 229--242, 2002. Google ScholarDigital Library
- E. Ng and H. Zhang. Predicting internet network distance with coordinates-based approaches. In Proc. IEEE INFOCOM, 2002.Google ScholarCross Ref
- S. Ong, P. Denholm, and E. Doris. The impacts of commercial electric utility rate structure elements on the economics of photovoltaic systems. Technical Report NREL/TP-6A2-46782, National Renewable Energy Laboratory, 2010.Google ScholarCross Ref
- E. Pakbaznia and M. Pedram. Minimizing data center cooling and server power costs. In Proc. ISLPED, 2009. Google ScholarDigital Library
- J. Pang, A. Akella, A. Shaikh, B. Krishnamurthy, and S. Seshan. On the responsiveness of DNS-based network control. In Proc. IMC, 2004. Google ScholarDigital Library
- M. Pathan, C. Vecchiola, and R. Buyya. Load and proximity aware request-redirection for dynamic load distribution in peering CDNs. In Proc. O™, 2008. 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, Aug. 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 INFOCOM, 2010. Google ScholarDigital Library
- R. T. Rockafellar. Convex Analysis. Princeton University Press, 1970.Google Scholar
- R. Stanojevic and R. Shorten. Distributed dynamic speed scaling. In Proc. IEEE INFOCOM, 2010. Google ScholarDigital Library
- W. Theilmann and K. Rothermel. Dynamic distance maps of the internet. In Proc. IEEE INFOCOM, 2001.Google Scholar
- E. Thereska, A. Donnelly, and D. Narayanan. Sierra: a power-proportional, distributed storage system. Technical Report MSR-TR-2009-153, Microsoft Research, 2009.Google Scholar
- O. S. Unsal and I. Koren. System-level power-aware deisgn techniques in real-time systems. Proc. IEEE, 91(7):1055--1069, 2003.Google ScholarCross Ref
- R. Urgaonkar, U. C. Kozat, K. Igarashi, and M. J. Neely. Dynamic resource allocation and power management in virtualized data centers. In IEEE NOMS, Apr. 2010.Google ScholarCross Ref
- P. Wendell, J. W. Jiang, M. J. Freedman, and J. Rexford. Donar: decentralized server selection for cloud services. In Proc. ACM Sigcomm, pages 231--242, 2010. Google ScholarDigital Library
- A. Wierman, L. L. H. Andrew, and A. Tang. Power-aware speed scaling in processor sharing systems. In Proc. IEEE INFOCOM, 2009.Google ScholarCross Ref
Index Terms
- Greening geographical load balancing
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