Advertisement

Energy Efficiency

, Volume 9, Issue 5, pp 1115–1144 | Cite as

Energy management for the homogeneous server clusters offering web services

  • Cheng-Jen TangEmail author
  • Miau-Ru Dai
  • Chi-Cheng Chuang
  • Yu-Sheng Chiu
Original Article
  • 244 Downloads

Abstract

The population of the Internet users has exceeded 2.4 billion. Data centers provide Internet services to fulfill the demand from these users. Many data centers adopt the cluster-based server systems to host the required Internet services. These server systems consume significant amount of energy, but much of the power is used to maintain service capacity during idle or low workload periods. This paper surveys some recent approaches addressing this issue. As learned from the traditional telephone call center planning processes, a queueing model is adopted to model the server clusters with homogeneous architecture. By analyzing the model, a set of the factors affecting the energy cost is identified. Based on the identified factors, an on-line energy management technique is then deigned. The proposed approach is simulated with a real-world workload trace. The simulation result shows that approximately 70 to 75 % of the originally consumed energy can be saved.

Keywords

Homogeneous server cluster Queueing model Data center energy management Energy efficient web services 

References

  1. Energy star program (2013). Requirements product specification for computer servers, version 2.0.Google Scholar
  2. Openadr 2.0 profile specification (2013). Document Number: 20120912-1.Google Scholar
  3. Ahmad, F., & Vijaykumar, T.N. (2010). Joint optimization of idle and cooling power in data centers while maintaining response time. SIGARCH Computing Archi. News, 38(1). doi: 10.1145/1735970.1736048.
  4. Albadi, M., & El-Saadany, E. (2007). Demand response in electricity markets: an overview. In Power Engineering Society General Meeting. doi: 10.1109/PES.2007.385728 (pp. 1–5).
  5. Allen, W., Bergman, K., Bernstein, K., Brill, K., Fortenbery, B., Giangrosso, P., Hughes, P., Joshi, Y., Johnson, M., Kelley, D., Kenny, K.P., Khaleel, M., Marquez, A., Kharitonov, D., Kuruganti, T., McIntyre, T., Perumalla, K., Page, C., Ridgley, R., Shalf, J., Tschudi, W., Stanley, J., & Yoo, B. (2009). Vision and roadmap: routing telecom and data centers toward efficient energy use. Tech. rep.: U.S. Department of Energy.Google Scholar
  6. Almeida, J., Almeida, V., & Yates, D.J. (1996). Measuring the behavior of a world-wide web server. In Tech. rep., Boston University Computer Science Department. CS 96-025.Google Scholar
  7. Anagnostopoulou, V., Biswas, S., Saadeldeen, H., Savage, A., Bianchini, R., Yang, T., Franklin, D., & Chong, F.T. (2012). Barely alive memory servers: keeping data active in a low-power state. ACM Journal on Emerging Technologies in Computing Systems, 8(4), 31:1–31:20. doi: 10.1145/2367736.2367742.
  8. Andres, P., & Spiwoks, M. (2002). Forecast quality matrix: a methodological survey of judging forecast quality of capital market forecasts. Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), 219(5+ 6), 513–542.Google Scholar
  9. Andrews, M. (2011). Recovery Act: Energy Efficiency of Data Networks through Rate Adaptation (EEDNRA)—Final Technical Report.Google Scholar
  10. Arlitt, M., & Jin, T. (2000). A workload characterization study of the 1998 world cup web site. IEEE Network, 14(3), 30–37. doi: 10.1109/65.844498.CrossRefGoogle Scholar
  11. Barford, P., & Crovella, M. (1998). Generating representative web workloads for network and server performance evaluation. SIGMETRICS Performance Evaluation Reviews, 26(1), 151–160. doi: 10.1145/277858.277897.CrossRefGoogle Scholar
  12. Barroso, L., & Hölzle, U. (2007). The case for energy-proportional computing. Computer, 40(12), 33–37. doi: 10.1109/MC.2007.443.CrossRefGoogle Scholar
  13. Bash, C., & Forman, G. (2007). Cool job allocation: measuring the power savings of placing jobs at cooling-efficient locations in the data center. In 2007 USENIX Annual Technical Conference on Proceedings of the USENIX Annual Technical Conference, ATC’07, pp. 29:1–29:6. USENIX Association, Berkeley, CA, USA. http://dl.acm.org/citation.cfm?id=1364385.1364414.
  14. Basmadjian, R., Bunse, C., Georgiadou, V., Giuliani, G., Klingert, S., Lovasz, G., & Majanen, M. (2010). Fit4green—energy aware ICT optimization policies. In Proceedings of the COST Action IC0804 on Energy Efficiency in Large Scale Distributed Systems.Google Scholar
  15. Basmadjian, R., Lovasz, G., Beck, M., De Meer, H., Hesselbach-Serra, X., Botero, J., Klingert, S., Perez Ortega, M., Lopez, J., Stam, A., van Krevelen, R., & Di Girolamo, M. (2013). A generic architecture for demand response: the All4Green approach.Google Scholar
  16. Basmadjian, R., Niedermeier, F., Fischer, A., Ortmeier, T., Dambeck, S., Skolnik-Korff, T., Lovsz, G., Giuliani, G., Klingert, S., & Kessel, M. (2013). All4green: final description of the energy provider/data centre sub-ecosystem components. In Tech. rep., All4Green: Active collaboration in data centre ecosystem to reduce energy consumption and GHG emissions, FP7 Project No 288674.Google Scholar
  17. Basu, S., Mukherjee, A., & Klivansky, S. (1996). Time series models for internet traffic. In INFOCOM ’96. Fifteenth Annual Joint Conference of the IEEE Computer Societies. Networking the Next Generation. Proceedings IEEE, (Vol. 2 pp. 611–620), DOI  10.1109/INFCOM.1996.493355.
  18. Beloglazov, A., Buyya, R., Lee, Y., & Zomaya, A. (2011). A taxonomy and survey of energy-efficient data centers and cloud computing systems. Advances in Computers, 82, 47–111.CrossRefGoogle Scholar
  19. vor dem Berge, M., Christmann, W., Volk, E., Wesner, S., Oleksiak, A., Piontek, T., Da Costa, G., & Pierson, J. (2012). Coolemall—models and tools for optimization of data center energy-efficiency. In Sustainable Internet and ICT for Sustainability (SustainIT), 2012, pp. 1–5.Google Scholar
  20. Berl, A., Klingert, S., Beck, M.T., & de Meer, H. (2013). Integrating data centres into demand-response management: a local case study. In Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE, pp. 4762–4767. doi: 10.1109/IECON.2013.6699905.
  21. Bertoncini, M., Pernici, B., Salomie, I., & Wesner, S. (2011) In Soffer, P., & Proper, E. (Eds.), Games: Green active management of energy in it service centres (Vol. 72, pp. 238–252). Berlin: Springer.Google Scholar
  22. Bianzino, A.P., Chaudet, C., Rossi, D., & Rougier, J.L. (2012). A survey of green networking research. IEEE Communications Surveys & Tutorials, 14(1), 3–20.CrossRefGoogle Scholar
  23. Bohrer, P., Elnozahy, E.N., Keller, T., Kistler, M., Lefurgy, C., McDowell, C., & Rajamony, R. (2002). The case for power management in web servers In Graybill, R., & Melhem, R. (Eds.), Power aware computing, (pp. 261–289). Norwell: Kluwer Academic Publishers. http://dl.acm.org/citation.cfm?id=783060.783075.
  24. Bolch, G., Greiner, S., de Meer, H., & Trivedi, K.S. (2006). Queueing networks and Markov chains: modeling and performance evaluation with computer science applications Wiley-Interscience.Google Scholar
  25. Boucher, T., Auslander, D., Bash, C., Federspiel, C., & Patel, C. (2004). Viability of dynamic cooling control in a data center environment. In Thermal and Thermomechanical Phenomena in Electronic Systems, 2004 . ITHERM ’04. The Ninth Intersociety Conference on, pp. 593–600, Vol. 1, DOI  10.1109/ITHERM.2004.1319229.
  26. Brown, L., Gans, N., Mandelbaum, A., Sakov, A., Shen, H., Zeltyn, S., & Zhao, L. (2005). Statistical analysis of a telephone call center. Journal of the American Statistical Association, 100(469), 36–50. doi: 10.1198/016214504000001808.MathSciNetCrossRefzbMATHGoogle Scholar
  27. Cao, J., Cleveland, W.S., Lin, D., & Sun, D.X. (2003). Internet traffic tends toward poisson and independent as the load increases. Lecture notes in statistics, (pp. 83–110). New York: Springer.Google Scholar
  28. Cappiello, C., Fugini, M., Gangadharan, G., Ferreira, A., Pernici, B., & Plebani, P. (2010) In Meersman, R., Dillon, T., & Herrero P. (Eds.), On the Move to Meaningful Internet Systems: OTM 2010 Workshops, Lecture Notes in Computer Science (Vol. 6428, pp. 6–7). Berlin Heidelberg: Springer.Google Scholar
  29. Cassandras, C.G., & Lafortune, S. (1999). Introduction to discrete event systems Vol. 11: Kluwer Academic Publishers.Google Scholar
  30. Chase, J.S., Anderson, D.C., Thakar, P.N., Vahdat, A.M., & Doyle, R.P. (2001). Managing energy and server resources in hosting centers. SIGOPS Operations Systems Reviews, 35(5), 103–116. doi: 10.1145/502059.502045.
  31. Chen, G., He, W., Liu, J., Nath, S., Rigas, L., Xiao, L., & Zhao, F. (2008). Energy-aware server provisioning and load dispatching for connection-intensive internet services. In Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation. USENIX Association, (Vol. 8 pp. 337–350).Google Scholar
  32. Chen, Y., Das, A., Qin, W., Sivasubramaniam, A., Wang, Q., & Gautam, N. (2005). Managing server energy and operational costs in hosting centers. SIGMETRICS Performance Evaluation Reviews, 33 (1), 303–314. doi: 10.1145/1071690.1064253.
  33. Chen, Y., Iyer, S., Liu, X., Milojicic, D., & Sahai, A. (2007). SLA decomposition: translating service level objectives to system level thresholds. In Proceedings of the Fourth International Conference on Autonomic Computing, ICAC ’07, pp. 3–. IEEE Computer Society, Washington, DC, USA. doi: 10.1109/ICAC.2007.36.
  34. Chung, K.H., Choi, M.S., & Ahn, K.S. (2007). A study on the packaging for fast boot-up time in the embedded linux. In Embedded and Real-Time Computing Systems and Applications, 2007. RTCSA 2007. 13th IEEE International Conference on, pp. 89–94. doi: 10.1109/RTCSA.2007.13.
  35. Cortez, P., Rio, M., Rocha, M., & Sousa, P. (2006). Internet traffic forecasting using neural networks. In Neural Networks, 2006. IJCNN ’06. International Joint Conference on, pp. 2635–2642. doi: 10.1109/IJCNN.2006.247142.
  36. Dagum, E.B., & Cholette, P.A. (2006). The components of time series. In Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series, Lecture Notes in Statistics, (Vol. 186 pp. 15–50). New York: Springer, doi: 10.1007/0-387-35439-5_2.
  37. Do, T., Krieger, U., & Chakka, R. (2008). Performance modeling of an apache web server with a dynamic pool of service processes. Telecommunication Systems, 39 (2), 117–129. doi: 10.1007/s11235-008-9116-y.CrossRefGoogle Scholar
  38. Economou, D., Rivoire, S., Kozyrakis, C., & Ranganathan, P. (2006). Full-system power analysis and modeling for server environments. In Inproceedings of Workshop on Modeling, Benchmarking, and Simulation, pp. 70–77.Google Scholar
  39. Elnozahy, E., Kistler, M., & Rajamony, R. (2003). Energy-efficient server clusters In Falsafi, B., & Vijaykumar, T. (Eds.), Power-Aware Computer Systems, Lecture Notes in Computer Science (Vol. 2325, pp. 179–197). Berlin Heidelberg: Springer.Google Scholar
  40. Environmental Protection Agency (2007). Report to congress on server and data center energy efficiency: public law 109–431 Tech. rep. Environmental Protection Agency U.S.Google Scholar
  41. European Commission and Others: Fp7 in brief—how to get involved in the EU 7th framework program for research (2007). Tech. rep., Office for Official Publications of the European Communities, Luxembourg.Google Scholar
  42. Fakhim, B., Srinarayana, N., Behnia, M., & Armfield, S. (2013). Thermal performance of data centers-rack level analysis. IEEE Transactions on Components, Packaging and Manufacturing Technology, 3(5), 792–799. doi: 10.1109/TCPMT.2013.2248195.CrossRefGoogle Scholar
  43. Fan, G.F., Qing, S., Wang, H., Hong, W.C., & Li, H.J. (2013). Support vector regression model based on empirical mode decomposition and auto regression for electric load forecasting. Energies, 6(4), 1887–1901. doi: 10.3390/en6041887.CrossRefGoogle Scholar
  44. Faruqui, A., Hledik, R., Newell, S., & Pfeifenberger, J. (2007). The power of five percent: how dynamic pricing can save $35 billion in electricity costs. In Tech. rep., The Brattle Group. http://sites.energetics.com/madri/pdfs/ArticleReport2441.pdf.
  45. Ferreira, J., Callou, G., & Maciel, P. (2013). A power load distribution algorithm to optimize data center electrical flow. Energies, 6(7), 3422–3443. doi: 10.3390/en6073422. http://www.mdpi.com/1996-1073/6/7/3422.
  46. Francini, A. (2012). Selection of a rate adaptation scheme for network hardware. In INFOCOM, 2012 Proceedings IEEE. doi: 10.1109/INFCOM.2012.6195710 (pp. 2831–2835).
  47. Gans, N., Koole, G., & Mandelbaum, A. (2003). Telephone call centers: tutorial, review, and research prospects. Manufacturing & Service Operations Management, 5(2), 79–141. doi: 10.1287/msom.5.2.79.16071.
  48. Ganti, V., & Ghatikar, G. (2012). Smart grid as a driver for energy-intensive industries: a data center case study. In Tech. rep., Lawrence Berkeley National Laboratory. LBNL-6104E.Google Scholar
  49. Ghosh, P., Roy, N., Basu, K., Das, S., Wilson, P., & Das, P. (2007). A case study-based performance evaluation framework for CSCF processes on a blade-server. In Networking and Services, 2007. ICNS. Third International Conference on, pp. 87–87. doi: 10.1109/ICNS.2007.2.
  50. Guerra, R., Leite, J., & Fohler, G. (2008). Attaining soft real-time constraint and energy-efficiency in web servers. In Proceedings of the 2008 ACM symposium on Applied computing, SAC ’08, pp. 2085–2089. ACM, New York. doi: 10.1145/1363686.1364189.
  51. Gurumurthi, S., Sivasubramaniam, A., Kandemir, M., & Franke, H. (2003). DRPM: dynamic speed control for power management in server class disks. In Computer Architecture, 2003. Proceedings. 30th Annual International Symposium on, pp. 169 – 179. doi: 10.1109/ISCA.2003.1206998.
  52. Harris, A. (2010). Distributed caching via memcached. In Pro ASP.NET 4 CMS, pp. 165–196. Apress. doi: 10.1007/978-1-4302-2713-7_6.
  53. Hoelzle, U., & Barroso, L.A. (2009). The Datacenter as a computer: an introduction to the design of warehouse-scale machines, 1st: Morgan and Claypool Publishers.Google Scholar
  54. Hoong, P.K., Tan, I.K.T., & Keong, C.Y. (2012). Bittorrent network traffic forecasting with arma. arXiv:CoRRabs/1208.1896.
  55. Isci, C., & Martonosi, M. (2003). Runtime power monitoring in high-end processors: methodology and empirical data. In Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture, MICRO 36, pp. 93–. IEEE Computer Society, Washington, DC. http://dl.acm.org/ citation.cfm?id=956417.956567.
  56. Joe, I., & Lee, S.C. (2011). Bootup time improvement for embedded linux using snapshot images created on boot time. In Next Generation Information Technology (ICNIT), 2011 The 2nd International Conference on, pp. 193–196.Google Scholar
  57. Joseph, R., & Martonosi, M. (2001). Run-time power estimation in high performance microprocessors. In Proceedings of the 2001 international symposium on Low power electronics and design, ISLPED ’01. doi: 10.1145/383082.383119 (pp. 135–140). New York: ACM.
  58. Kalman, R.E., & et al. (1960). A new approach to linear filtering and prediction problems. Journal of basic Engineering, 82(1), 35–45.CrossRefGoogle Scholar
  59. Karnouskos, S., da Silva, P.G., & Ilic, D. (2011). Assessment of high-performance smart metering for the web service enabled smart grid era. SIGSOFT Software Engineering Notes, 36(5), 133–144. doi: 10.1145/1958746.1958768.CrossRefGoogle Scholar
  60. Kelly, T., & Reeves, D. (2001). Optimal web cache sizing: scalable methods for exact solutions. Computer Communications, 24(2), 163–173. doi: 10.1016/S0140-3664(00)00311-X. http://www.sciencedirect.com/science/article/pii/S014036640000311X.
  61. Kimura, H., Sato, M., Hotta, Y., Boku, T., & Takahashi, D. (2006). Emprical study on reducing energy of parallel programs using slack reclamation by DVFS in a power-scalable high performance cluster. In Cluster Computing, 2006 IEEE International Conference on, pp. 1–10. doi: 10.1109/CLUSTR.2006.311839.
  62. Klingert, S., Basmadjian, R., Dupont, C., Somov, A., Georgiadou, V., & Girolamo, M.D. (2012). Fit4green reader’s digest technical aspects. http://www.fit4green.eu/sites/default/files/attachments/ documents/ReadersGuide_FINAL.pdf.
  63. Koole, G., & Mandelbaum, A. (2002). Queueing models of call centers: an introduction. Annals of Operations Research, 113(1-4), 41–59. doi: 10.1023/A:1020949626017.MathSciNetCrossRefzbMATHGoogle Scholar
  64. Lama, P., & Zhou, X. (2012). Efficient server provisioning with control for end-to-end response time guarantee on multitier clusters. IEEE Transactions on Parallel and Distributed Systems, 23(1), 78–86. doi: 10.1109/TPDS.2011.88.CrossRefGoogle Scholar
  65. Lefurgy, C., Wang, X., & Ware, M. (2007). Server-level power control. doi: 10.1109/ICAC.2007.35.
  66. Li, X., & Zekavat, S. (2009). Traffic pattern prediction based spectrum sharing for cognitive radios. Cognitive radio systems, 77–95.Google Scholar
  67. Liao, X., Hu, L., & Jin, H. (2010). Energy optimization schemes in cluster with virtual machines. Cluster Computing, 13(2), 113–126. doi: 10.1007/s10586-009-0110-2.CrossRefGoogle Scholar
  68. Lien, C.H., Bai, Y.W., & Lin, M.B. (2007). Estimation by software for the power consumption of streaming-media servers. 56, 5, 1859 –1870. doi: 10.1109/TIM.2007.904554.Google Scholar
  69. Liu, X., Sha, L., Diao, Y., Froehlich, S., Hellerstein, J., & Parekh, S. (2003). Online response time optimization of apache web server. In Jeffay, K., Stoica, I., & Wehrle, K. (Eds.), (Vol. 2707 pp. 461–478). Berlin: Springer.Google Scholar
  70. Liu, Y., Yang, H., Dick, R., Wang, H., & Shang, L. (2007). Thermal vs energy optimization for DVFS-enabled processors in embedded systems. In Quality Electronic Design, 2007. ISQED ’07. 8th International Symposium on, pp. 204–209. doi: 10.1109/ISQED.2007.158.
  71. Liu, Y., & Zhu, H. (2010). A survey of the research on power management techniques for high-performance systems. Software: Practice and Experience, 40(11), 943–964.Google Scholar
  72. Liu, Z., Lin, M., Wierman, A., Low, S.H., & Andrew, L.L. (2011). Geographical load balancing with renewables. SIGMETRICS Performance Evaluation Reviews, 39(3), 62–66. doi: 10.1145/2160803.2160862.CrossRefGoogle Scholar
  73. Liu, Z., Wierman, A., Chen, Y., Razon, B., & Chen, N. (2013). Data center demand response: avoiding the coincident peak via workload shifting and local generation. doi: 10.1145/2494232.2465740, (Vol. 41 pp. 341–342).
  74. Lodi, G., Panzieri, F., Rossi, D., & Turrini, E. (2007). Sla-driven clustering of QoS-aware application servers. IEEE Transactions on Software Engineering, 33(3), 186–197. doi: 10.1109/TSE.2007.28.
  75. Loper, J., & Parr, S. (2007). Energy efficiency in data centers: a new policy frontier. Environmental Quality Management, 16(4), 83–97.CrossRefGoogle Scholar
  76. Low, S., & Tang, K. (2011). Power minimization techniques for networked data centers. doi: 10.2172/1025358. http://www.osti.gov/scitech/servlets/purl/1025358.
  77. Masuda, H., Saitoh, A., Nakanishi, M., & Yasutome, S. (2005). Diskless linux system with unionfs for an educational computer center. In Proceedings of the 33rd annual ACM SIGUCCS fall conference, SIGUCCS ’05. doi: 10.1145/1099435.1099482 (pp. 207–210). New York: ACM.
  78. Mathys, C. (2012). Aggregated residential demand response using smart meters. Tech. rep. New York State Energy Research and Development Authority.Google Scholar
  79. Mehrotra, R., Dubey, A., Abdelwahed, S., & Tantawi, A. (2011). A power-aware modeling and autonomic management framework for distributed computing systems. In Handbook of Energy-Aware and Green Computing: Chapman and Hall/CRC Press.Google Scholar
  80. Mehta, H., Kanungo, P., & Chandwani, M. (2011). Decentralized content aware load balancing algorithm for distributed computing environments. In Proceedings of the International Conference & Workshop on Emerging Trends in Technology, ICWET ’11. doi: 10.1145/1980022.1980102 (pp. 370–375). New York: ACM.
  81. Meisner, D., Gold, B.T., & Wenisch, T.F. (2009). Powernap: eliminating server idle power. SIGPLAN Not, 44(3), 205–216. doi: 10.1145/1508284.1508269.CrossRefGoogle Scholar
  82. Meliksetian, D., Yu, F.F.K., & Chen, C.Y. (2000). Methodologies for designing video servers. IEEE Transactions on Multimedia, 2(1), 62–69. doi: 10.1109/6046.825798.CrossRefGoogle Scholar
  83. Menasce, D. (2003). Web server software architectures. IEEE Internet Computing, 7(6), 78–81. doi: 10.1109/MIC.2003.1250588.CrossRefGoogle Scholar
  84. Moore, J.D., Chase, J.S., Ranganathan, P., & Sharma, R.K. (2005). Making scheduling “cool” : temperature-aware workload placement in data centers. In USENIX annual technical conference, General Track (pp. 61–75).Google Scholar
  85. Morrison, J., & Martin, D. (2006). Cycle time approximations for the g/g/m queue subject to server failures and cycle time offsets with applications. In Advanced Semiconductor Manufacturing Conference, 2006. ASMC 2006. The 17th Annual SEMI/IEEE. doi: 10.1109/ASMC.2006.1638777 (pp. 322–326).
  86. Mosberger, D., & Jin, T. (1998). Httperf—a tool for measuring web server performance. SIGMETRICS Performance Evaluation Reviews, 26(3), 31–37. doi: 10.1145/306225.306235.CrossRefGoogle Scholar
  87. Nielsen, J. (1993). Usability Engineering Morgan Kaufmann.Google Scholar
  88. Nielsen, J. (2010). Website response times. Nielsen Norman Group. http://www.nngroup.com/articles/website-response-times/.
  89. Pakbaznia, E., & Pedram, M. (2009). Minimizing data center cooling and server power costs. In Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design, ISLPED ’09. doi: 10.1145/1594233.1594268 (pp. 145–150). New York: ACM.
  90. Papagiannaki, K., Taft, N., Zhang, Z.L., & Diot, C. (2005). Long-term forecasting of internet backbone traffic. IEEE Transactions on Neural Networks, 16(5), 1110–1124. doi: 10.1109/TNN.2005.853437.
  91. Park, S.H., Lee, T.H., & Chung, K.D. (2006). Design of a NAND flash memory file system to improve system boot time. JIPS, 2(3), 147–152.Google Scholar
  92. Pernici, B., Cappiello, C., Fugini, M., Plebani, P., Vitali, M., Salomie, I., Cioara, T., Anghel, I., Henis, E., Kat, R., Chen, D., Goldberg, G., Berge, M., Christmann, W., Kipp, A., Jiang, T., Liu, J., Bertoncini, M., Arnone, D., & Rossi, A. (2012). Energy Efficient Data Centers. In Huusko, J., Meer, H., Klingert, S., & Somov, A. (Eds.) Lecture Notes in Computer Science. doi: 10.1007/978-3-642-33645-4_1, (Vol. 7396 pp. 1–12). Berlin: Springer.
  93. Pfeiffer, C., & Kulali, E. (2012). Recovery act: data center transfer from “Always On” to “Always Available” to Reduce Power. http://www.osti.gov/scitech/servlets/purl/1092145.
  94. Pinheiro, E., Bianchini, R., Carrera, E., & Heath, T. (2001). Load balancing and unbalancing for power and performance in cluster-based systems. In Workshop on compilers and operating systems for low power, (Vol. 180 pp. 182–195).Google Scholar
  95. Qian, H., Li, F., & Medhi, D. (2012). On energy-aware aggregation of dynamic temporal demand in cloud computing. In Communication Systems and Networks (COMSNETS), 2012 Fourth International Conference on, pp. 1–6. doi: 10.1109/COMSNETS.2012.6151370.
  96. Quan, D., Basmadjian, R., Meer, H., Lent, R., Mahmoodi, T., Sannelli, D., Mezza, F., Telesca, L., & Dupont, C. (2012). Energy efficient resource allocation strategy for cloud data centres In E. Gelenbe, R. Lent, & G. Sakellari (Eds.), Computer and Information Sciences II, pp. 133–141. London: Springer.Google Scholar
  97. Quiroz, A., Kim, H., Parashar, M., Gnanasambandam, N., & Sharma, N. (2009). Towards autonomic workload provisioning for enterprise grids and clouds. doi: 10.1109/GRID.2009.5353066  10.1109/GRID.2009.5353066.
  98. Rahimi, F., & Ipakchi, A. (2010). Demand response as a market resource under the smart grid paradigm. IEEE Transactions on Smart Grid, 1(1), 82–88. doi: 10.1109/TSG.2010.2045906  10.1109/TSG.2010.2045906.
  99. Ranganathan, P., Leech, P., Irwin, D., & Chase, J. (2006). Ensemble-level power management for dense blade servers. SIGARCH Computer Architecture News, 34(2), 66–77. doi: 10.1145/1150019.1136492.
  100. Rao, L., Liu, X., Xie, L., & Liu, W. (2010). Minimizing electricity cost: optimization of distributed internet data centers in a multi-electricity-market environment. In INFOCOM, 2010 Proceedings IEEE, pp. 1 –9. doi: 10.1109/INFCOM.2010.5461933.
  101. Ren, C., Wang, D., Urgaonkar, B., & Sivasubramaniam, A. (2012). Carbon-aware energy capacity planning for datacenters. In Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), 2012 IEEE 20th International Symposium on, pp. 391–400. IEEE.Google Scholar
  102. Rivoire, S., Ranganathan, P., & Kozyrakis, C. (2008). A comparison of high-level full-system power models. In Proceedings of the 2008 conference on Power aware computing and systems, HotPower’08 (pp. 3–3). Berkeley: USENIX Association.Google Scholar
  103. Rochlin, C. (2009). The alchemy of demand response: turning demand into supply. The Electricity Journal, 22(9), 10–25. doi: 10.1016/j.tej.2009.09.004. http://www.sciencedirect.com/science/article/pii/S1040619009002504.
  104. Schmidt, D., & Wehn, N. (2009). Dram power management and energy consumption: a critical assessment. In Proceedings of the 22nd Annual Symposium on Integrated Circuits and System Design: Chip on the Dunes, SBCCI ’09. doi: 10.1145/1601896.1601937 (pp. 32:1–32:5). New York: ACM.
  105. Sharma, R., Bash, C., Patel, C., Friedrich, R., & Chase, J. (2005). Balance of power: dynamic thermal management for internet data centers. doi: 10.1109/MIC.2005.10, (Vol. 9 pp. 42 – 49).
  106. Sherden, W.A. (1998). The fortune sellers: the big business of selling and buying predictions: Wiley.Google Scholar
  107. Sis, L., Forns, R.B., Napolitano, A., & Salom, J. (2012). White paper on energy- and heat-aware metrics for computing modules. In Tech. rep. CoolEmAll: Platform for optimising the design and operation of modular configurable IT infrastructures and facilities with resource-efficient cooling.Google Scholar
  108. Smith, W.E., Trivedi, K., Tomek, L., & Ackaret, J. (2008). Availability analysis of blade server systems. IBM Systems Journal, 47(4), 621–640. doi: 10.1147/SJ.2008.5386524.
  109. Standard Performance Evaluation Corporation (2013). Spec’s benchmarks and published results. http://www.spec.org/.
  110. Strobel, C.D. (2009). American recovery and reinvestment act of 2009. Journal of Corporate Accounting & Finance, 20(5), 83–85. doi: 10.1002/jcaf.20519.CrossRefGoogle Scholar
  111. Summers, J., Brecht, T., Eager, D., & Wong, B. (2012). Methodologies for generating http streaming video workloads to evaluate web server performance. In Proceedings of the 5th Annual International Systems and Storage Conference, SYSTOR ’12. doi: 10.1145/2367589.2367602 (pp. 2:1–2:12). New York: ACM.
  112. Tabachnick, B., & Fidell, L. (2012). Using multivariate statistics (6th edition) pearson.Google Scholar
  113. Takeda, S., & Takemura, T. (2010). A rank-based VM consolidation method for power saving in datacenters. IPSJ Online Transactions, 3, 88–96.CrossRefGoogle Scholar
  114. Tang, C.J., Dai, M.R., Chuang, C.C., Chiu, Y.S., & Lin, W. (2014). A load control method for small data centers participating in demand response programs. Future Generation Computer Systems, 32(0), 232–245. doi: 10.1016/j.future.2013.07.020. http://www.sciencedirect.com/science/article/pii/S0167739X13001659.CrossRefGoogle Scholar
  115. Tang, C.J., He, H.C., Dai, M.R., & Chuang, C.C. (2012). Evaluating memory size for energy efficient sorting. Bulletin of Networking Computing Systems, and Software, 1(1), 1–13.Google Scholar
  116. The Internet Traffic Archive (2008). Traces available in the internet traffic archive. http://ita.ee.lbl.gov/html/traces.html.
  117. Tijms, H.C. (2003). A first course in stochastic models: Wiley.Google Scholar
  118. Trivedi, K.S. (2001). Probability and statistics with reliability, queuing, and computer science applications, Vol. 2002. Prentice-hall Englewood Cliffs.Google Scholar
  119. Valentini, G.L., Lassonde, W., Khan, S.U., Min-Allah, N., Madani, S.A., Li, J., Zhang, L., Wang, L., Ghani, N., Kolodziej, J., & et al. (2011). An overview of energy efficiency techniques in cluster computing systems. Cluster Computing, 1–13.Google Scholar
  120. Varsamopoulos, G., Abbasi, Z., & Gupta, S. (2010). Trends and effects of energy proportionality on server provisioning in data centers. In High Performance Computing (HiPC), 2010 International Conference on, pp. 1 –11. doi: 10.1109/HIPC.2010.5713198.
  121. Vasan, A., Sivasubramaniam, A., Shimpi, V., Sivabalan, T., & Subbiah, R. (2010). Worth their watts?—an empirical study of datacenter servers. In High Performance Computer Architecture (HPCA), 2010 IEEE 16th International Symposium on, pp. 1 –10. doi: 10.1109/HPCA.2010.5463056.
  122. Volk, E., Rathgeb, D., & Oleksiak, A. (2013). Coolemall—optimising cooling efficiency in data centres. Computer Science - Research and Development, 1–9. doi: 10.1007/s00450-013-0246-4.
  123. Wang, C., Zhang, X., Yan, H., & Zheng, L. (2008). An internet traffic forecasting model adopting radical based on function neural network optimized by genetic algorithm. In Knowledge Discovery and Data Mining, 2008. WKDD 2008. First International Workshop on, pp. 367–370. doi: 10.1109/WKDD.2008.13.
  124. Wang, L., von Laszewski, G., Dayal, J., & Wang, F. (2010). Towards energy aware scheduling for precedence constrained parallel tasks in a cluster with DVFS. In Cluster, Cloud and Grid Computing (CCGrid), 2010 10th IEEE/ACM International Conference on, pp. 368–377. doi: 10.1109/CCGRID.2010.19.
  125. Wang, S., Liu, J., Chen, J.J., & Liu, X. (2011). Powersleep: a smart power-saving scheme with sleep for servers under response time constraint. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 1(3), 289–298. doi: 10.1109/JETCAS.2011.2167532.CrossRefGoogle Scholar
  126. Wang, X., Chen, M., & Fu, X. (2010). Mimo power control for high-density servers in an enclosure. IEEE Transactions on Parallel and Distributed Systems, 21 (10), 1412 –1426. doi: 10.1109/TPDS.2010.31.CrossRefGoogle Scholar
  127. Whitt, W. (1999). Dynamic staffing in a telephone call center aiming to immediately answer all calls. Operations Research Letters, 24(5), 205–212. doi:10.1016/S0167-6377(99)00022-X. http://www.sciencedirect.com/science/article/pii/S016763779900022X.
  128. Wierman, A., Andrew, L.L., & Tang, A. (2012). Power-aware speed scaling in processor sharing systems: optimality and robustness. Performance Evaluation, 69(12), 601– 622. doi: 10.1016/j.peva.2012.07.002. http://www.sciencedirect.com/science/article/pii/S0166531612000697.
  129. You, C., & Chandra, K. (1999). Time series models for internet data traffic. In Local Computer Networks, 1999. LCN ’99. Conference on, pp. 164–171. doi: 10.1109/LCN.1999.802013.
  130. Yu, M., Gong, J., Tang, J., & Zhu, H. (2013). The method of staffing a call center with delay information considering the customers’ behavior. In Control and Decision Conference (CCDC), 2013 25th Chinese, pp. 4723–4727. doi: 10.1109/CCDC.2013.6561788.
  131. Zedlewski, J., Sobti, S., Garg, N., Zheng, F., Krishnamurthy, A., & Wang, R. (2003). Modeling hard-disk power consumption. In Proceedings of the 2nd USENIX Conference on File and Storage Technologies, vol. 28, pp. 32–72.Google Scholar
  132. Zhani, M.F., Elbiaze, H., & Kamoun, F. (2009). Analysis and prediction of real network traffic. Journal of networks, 4(9), 855–865.CrossRefGoogle Scholar
  133. Zwerling, J. (2012). Amare: standout of the year was linsanity. ESPNNewYork.com.

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Electrical EngineeringTatung UniversityTaipeiRepublic of China
  2. 2.Smart Grid NBDDelta Networks, Inc.TaipeiRepublic of China
  3. 3.Institute for Information IndustryInstitute for Information IndustryTapei City 105Republic of China

Personalised recommendations