Advertisement

GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy

  • Yanwei Zhang
  • Yefu Wang
  • Xiaorui Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7049)

Abstract

To reduce the negative environmental implications (e.g., CO 2 emission and global warming) caused by the rapidly increasing energy consumption, many Internet service operators have started taking various initiatives to operate their cloud-scale data centers with renewable energy. Unfortunately, due to the intermittent nature of renewable energy sources such as wind turbines and solar panels, currently renewable energy is often more expensive than brown energy that is produced with conventional fossil-based fuel. As a result, utilizing renewable energy may impose a considerable pressure on the sometimes stringent operation budgets of Internet service operators. Therefore, two key questions faced by many cloud-service operators are 1) how to dynamically distribute service requests among data centers in different geographical locations, based on the local weather conditions, to maximize the use of renewable energy, and 2) how to do that within their allowed operation budgets.

In this paper, we propose GreenWare, a novel middleware system that conducts dynamic request dispatching to maximize the percentage of renewable energy used to power a network of distributed data centers, subject to the desired cost budget of the Internet service operator. Our solution first explicitly models the intermittent generation of renewable energy, e.g., wind power and solar power, with respect to varying weather conditions in the geographical location of each data center. We then formulate the core objective of GreenWare as a constrained optimization problem and propose an efficient request dispatching algorithm based on linear-fractional programming (LFP). We evaluate GreenWare with real-world weather, electricity price, and workload traces. Our experimental results show that GreenWare can significantly increase the use of renewable energy in cloud-scale data centers without violating the desired cost budget, despite the intermittent supplies of renewable energy in different locations and time-varying electricity prices and workloads.

Keywords

Renewable Energy Data Center Wind Turbine Wind Power Wind Farm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
  2. 2.
    Dominion Virginia Power, http://www.dom.com/
  3. 3.
    Energy modality comparison based on projected cents per kilowatt-hour, http://peswiki.com/
  4. 4.
    Green House Data: Greening the data center, http://www.greenhousedata.com/
  5. 5.
    Los Angeles Department of Water & Power, http://www.ladwp.com/
  6. 6.
    Measurement and Instrumentation data center, http://www.nrel.gov/midc/
  7. 7.
    The Running Time of the Simplex Method, http://www.mpi-inf.mpg.de
  8. 8.
    Solar Electricity Prices, http://solarbuzz.com/
  9. 9.
  10. 10.
    NYISO (1999), http://www.nyiso.com/
  11. 11.
    Solar Power at Data Center Scale (2009), http://www.datacenterknowledge.com/
  12. 12.
    Google Buys 20 Years’ Worth of Wind Energy To Power Data centers (2010), http://www.huffingtonpost.com/
  13. 13.
    Ahmad, F., Vijaykumar, T.N.: Joint optimization of idle and cooling power in data centers while maintaining response time. In: ASPLOS (2010)Google Scholar
  14. 14.
    Bolch, G., Greiner, S., de Meer, H., Trivedi, K.S.: Queueing Networks and Markov Chains. Wiley Interscience (2005)Google Scholar
  15. 15.
    Brown, M., Renau, J.: Rerack: Power simulation for data centers with renewable energy generation. In: GreenMetrics (2011)Google Scholar
  16. 16.
    Castaner, L., Silvestre, S.: Modelling Photovoltaic Systems Using PSpice. John Wiley & Sons (2002)Google Scholar
  17. 17.
    Chase, J.S., Anderson, D.C., Thakar, P.N., Vahdat, A.M., Doyle, R.P.: Managing energy and server resources in hosting centers. In: SOSP (2001)Google Scholar
  18. 18.
    Chen, G., He, W., Liu, J., Nath, S., Rigas, L., Xiao, L., Zhao, F.: Energy-aware server provisioning and load dispatching for connection-intensive internet services. In: NSDI (2008)Google Scholar
  19. 19.
    Ding, J., Radhakrishnan, R.: A new method to determine the optimum load of a real solar cell using the lambert w-function. Solar Energy Materials and Solar Cell (2008)Google Scholar
  20. 20.
    Elnozahy, E.N.M., Kistler, J.J., Rajamony, R.: Energy-Efficient Server Clusters. In: Falsafi, B., VijayKumar, T.N. (eds.) PACS 2002. LNCS, vol. 2325, pp. 179–196. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  21. 21.
    Kariniotakis, G.N., Stavrakakis, G.S., Nogaret, E.F.: Wind power forecasting using advanced neural networks models. IEEE Transactions on Energy Conversion, 762–767 (1996)Google Scholar
  22. 22.
    Greenberg, A., Hamilton, J., Maltz, D.A., Patel, P.: The cost of a cloud: research problems in data center networks. ACM SIGCOMM Computer Communication Review (2008)Google Scholar
  23. 23.
    Heo, J., Henriksson, D., Liu, X., Abdelzaher, T.: Integrating adaptive components: An emerging challenge in performance-adaptive systems and a server farm case-study. In: RTSS (2007)Google Scholar
  24. 24.
    Hillier, F.S., Lieberman, G.J.: Introduction to Operations Research. McGraw-Hill (2005)Google Scholar
  25. 25.
  26. 26.
    Horvath, T., Abdelzaher, T., Skadron, K., Liu, X.: Dynamic voltage scaling in multitier web servers with end-to-end delay control. IEEE Transactions on Computers, 444–458 (2007)Google Scholar
  27. 27.
    Kothari, L.S., Mathur, P.C., Kapoor, A., Saxena, P., Sharma, R.P.: Determination of optimum load for a solar cell. Journal of Applied Physics, 5982–5984 (2009)Google Scholar
  28. 28.
    Le, K., Bianchini, R., Martonosi, M., Nguyen, T.D.: Cost- and energy-aware load distribution across data centers. In: HOTPOWER (2009)Google Scholar
  29. 29.
    Le, K., Bilgir, O., Bianchini, R., Martonosi, M., Nguyen, T.D.: Managing the cost, energy consumption, and carbon footprint of internet services. In: SIGMETRICS (2010)Google Scholar
  30. 30.
    Li, C., Qouneh, A., Li, T.: Characterizing and analyzing renewable energy driven data centers. In: SIGMETRICS (2011)Google Scholar
  31. 31.
    Li, C., Zhang, W., Cho, C.B., Li, T.: Solarcore: Solar energy driven multi-core architecture power management. In: HPCA (2011)Google Scholar
  32. 32.
    Li, J., Li, Z., Ren, K., Liu, X., Su, H.: Towards optimal electric demand management for internet data centers. In: Techreport (2010)Google Scholar
  33. 33.
    Lin, M., Wierman, A., Andrew, L.L.H., Thereska, E.: Dynamic right-sizing for power-proportional data centers. In: INFOCOM (2011)Google Scholar
  34. 34.
    Liu, Z., Lin, M., Wierman, A., Low, S.H., Andrew, L.L.H.: Greening geograhpical load balancing. In: SIGMETRICS (2011)Google Scholar
  35. 35.
    Patel, M.R.: Power systems: Design, Analysis, and Operation. CRC Press (2006)Google Scholar
  36. 36.
    Paukshto, M.V., Lovetskiy, K.: Invariance of single diode equation and its application. In: PVSC (2008)Google Scholar
  37. 37.
    Petru, T., Thiringer, T.: Modeling of wind turbines for power system studies. IEEE Transactions on Power Systems, 1132–1139 (2002)Google Scholar
  38. 38.
    Pistoia, G.: Battery Operated Devices and Systems: From Portable Electronics to Industrial Products. Elsevier (2011)Google Scholar
  39. 39.
    Qureshi, A., Weber, R., Balakrishnan, H., Guttag, J., Maggs, B.: Cutting the electric bill for internet-scale systems. In: SIGCOMM (2009)Google Scholar
  40. 40.
    Rao, L., Liu, X., Xie, L., Liu, W.: Minimizing electricity cost: optimization of distributed internet data centers in a multi-electricity-market environment. In: INFOCOM (2010)Google Scholar
  41. 41.
    Sera, D., Teodorescu, R., Rodriguez, P.: PV panel model based on datasheet values. In: ISIE (2007)Google Scholar
  42. 42.
    Stewart, C., Shen, K.: Some joules are more precious than others: Managing renewable energy in the datacenter. In: HOTPOWER (2009)Google Scholar
  43. 43.
    Thibodeau, P.: Wind power data center project planned in urban area (2008), http://www.computerworld.com/
  44. 44.
    United states environmental protection agency. Report to congress on server and data center energy efficiency (2007)Google Scholar
  45. 45.
    Urdaneta, G., Pierre, G., van Steen, M.: Wikipedia workload analysis for decentralized hosting. Elsevier Computer Networks 53(11), 1830–1845 (2009), http://www.globule.org/publi/WWADH_comnet2009.html CrossRefGoogle Scholar
  46. 46.
    Urgaonkar, B., Shenoy, P., Chandra, A., Goyal, P.: Dynamic provisioning of multi-tier internet applications. In: ICAC (2005)Google Scholar
  47. 47.
    Verma, A., Dasgupta, G., Nayak, T.K., De, P., Kothari, R.: Server workload analysis for power minimization using consolidation. In: USENIX ATC (2009)Google Scholar
  48. 48.
    Zhang, Y., Wang, Y., Wang, X.: Capping the electricity cost of cloud-scale data centers with impacts on power markets. In: HPDC (2011)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Yanwei Zhang
    • 1
  • Yefu Wang
    • 1
  • Xiaorui Wang
    • 1
    • 2
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of TennesseeKnoxville
  2. 2.Department of Electrical and Computer EngineeringThe Ohio State UniversityColumbus

Personalised recommendations