Advertisement

Energy and Power Efficiency in Cloud

  • Michał Karpowicz
  • Ewa Niewiadomska-SzynkiewiczEmail author
  • Piotr Arabas
  • Andrzej Sikora
Chapter
Part of the Computer Communications and Networks book series (CCN)

Abstract

Reduction of energy consumption is clearly one of the major technological challenges arising with development of cloud computing infrastructures. To meet the ever increasing demand for computing power, recent research efforts have been taking holistic view to energy-aware design of hardware, middleware, and data processing applications. Indeed, advances in hardware layer development require immediate improvements in the design of system control software. For this to be possible, new power management capabilities of hardware layer need to be exposed in the form of flexible Application Program Interfaces (APIs). Consequently, novel APIs and cluster management tools allow for system-wide regulation of energy consumption, capable of collecting and processing detailed cluster performance measurements, and taking real-time coordinated actions across the cloud infrastructure. This chapter presents an overview of techniques developed to improve energy efficiency of cloud computing. Power consumption models and energy usage profiles are presented together with energy efficiency measuring methods. Modeling of computing and network dynamics is discussed from the viewpoint of system identification theory, indicating basic experiment design problems and challenges. Novel approaches to cluster and network-wide energy usage optimisation are surveyed, including multi-level power and software control systems, energy-aware task scheduling, resource allocation algorithms and frameworks for backbone networks management. Software-development techniques and tools are also presented as a new promising way to reduce power consumption at the computing node level. Finally, energy-aware server-level and network-level control mechanisms are presented, including ACPI-compliant low power idle and service rate scaling solutions.

Keywords

Power Consumption Network Device Traffic Matrix Line Card Dynamic Power Management 
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.

Notes

Acknowledgments

This research was partially supported by the National Science Centre (NCN) under the grant no. 2015/17/B/ST6/01885.

References

  1. 1.
    ETP4HPC Strategic Research Agenda Achieving HPC leadership in Europe. www.etp4hpc.eu
  2. 2.
  3. 3.
  4. 4.
  5. 5.
    Abts, D., Marty, M.R., Wells, Ph.M., Klausler, P., Liu, H.: Energy proportional datacenter networks. SIGARCH Comput. Archit. News 38(3), 338–347 (2010). JuneGoogle Scholar
  6. 6.
    Al-Fares, M., Loukissas, M., Vahdat, A.: A scalable, commodity data center network architecture. In: Proceedings SIGCOMM 2008 Conference on Data Communications, Seattle, WA, pp. 63–74 (2008)Google Scholar
  7. 7.
    Arabas, P., Karpowicz, M.: Server power consumption: measurements and modeling with msrs. In: Proceedings AUTOMATION-2016, March 2–4, 2016, Warsaw, Poland, pp. 233–244. Springer International Publishing (2016)Google Scholar
  8. 8.
    Arabas, P., Malinowski, K., Sikora, A.: On formulation of a network energy saving optimization problem. In: Proceedings of 4th International Conference on Communications and Electronics (ICCE 2012), pp. 122–129 (2012)Google Scholar
  9. 9.
    Arabas, P., Karpowicz, M.: Server power consumption: measurements and modeling with MSRs. In: Challenges in Automation, Robotics and Measurement Techniques, pp. 233–244. Springer (2016)Google Scholar
  10. 10.
    Arjona Aroca, J., Chatzipapas, A., Fernández Anta, A., Mancuso. V.: A measurement-based analysis of the energy consumption of data center servers. In: Proceedings 5th International Conference on Future Energy Systems, pp. 63–74. ACM (2014)Google Scholar
  11. 11.
    Åström, K.J., Wittenmark, B.: Computer-controlled systems: theory and design. Dover Publications, Mineola (2011)Google Scholar
  12. 12.
    Åström, K.J., Hägglund, T.: Advanced PID control. ISA-The Instrumentation, Systems, and Automation Society; Research Triangle Park, NC 27709 (2006)Google Scholar
  13. 13.
    Andr Barroso, L., Hlzle, U.: The case for energy-proportional computing. IEEE Comput. 40(12), 3337 (2007)Google Scholar
  14. 14.
    André Barroso, L., Clidaras, J., Hölzle, U.: The datacenter as a computer: an introduction to the design of warehouse-scale machines. Morgan & Claypool Publishers (2013)Google Scholar
  15. 15.
    Benito, M., Vallejo, E., Beivide, R.: On the use of commodity ethernet technology in exascale hpc systems. In: Proceedings IEEE 22nd International Conference on High Performance Computing (HiPC), pp. 254–263 (2015)Google Scholar
  16. 16.
    Bertsekas, D.P.: Dynamic Programming and Optimal Control, 3rd edn. Athena Scientific, Belmont (2005)Google Scholar
  17. 17.
    Bianco, F., Cucchietti, G., Griffa, G.: Energy consumption trends in the next generation access network – a telco perspective. In: Proceedings 29th International Telecommunication Energy Conference (INTELEC 2007), pp. 737–742 (2007)Google Scholar
  18. 18.
    Bianzino, A.P., Chaudet, C., Rossi, D., Rougier, J.-L.: A survey of green networking research. IEEE Commun. Surveys Tutorials 2 (2012)Google Scholar
  19. 19.
    Bianzino, A.P., Chiaraviglio, L., Mellia, M.: GRiDA: a green distributed algorithm for backbone networks. In: Online Conference on Green Communications (GreenCom 2011), pp. 113–119. IEEE (2011)Google Scholar
  20. 20.
    Bolla, R., Bruschi, R.: Energy-aware load balancing for parallel packet processing engines. In: Online Conference on Green Communications (GreenCom), pp. 105–112. IEEE (2011)Google Scholar
  21. 21.
    Bolla, R., Bruschi, R., Davoli, F., Cucchietti, F.: Energy efficiency in the future internet: a survey of existing approaches and trends in energy-aware fixed network infrastructures. IEEE Commun. Surveys Tutorials 13, 223–244 (2011)CrossRefGoogle Scholar
  22. 22.
    Bolla, R., Bruschi, R., Davoli, F., Lago, P., Bakay, A., Grosso, R., Kamola, M., Karpowicz, M., Koch, L., Levi, D., Parladori, P., Suino, D.: Large-scale validation and benchmarking of a network of power-conservative systems using etsi’s green abstraction layer. Trans. Emerg. Tel. Tech. 2016(27), 451–468 (2016)CrossRefGoogle Scholar
  23. 23.
    Bolla, R., Bruschi, R., Ranieri. A.: Green support for pc-based software router: performance evaluation and modeling. In: ICC’09 Communications International Conference, pp. 1–6. IEEE (2009)Google Scholar
  24. 24.
    Bolla, R., et al.: Econet deliverable d2.1 end-user requirements, technology, specifications and benchmarking methodology. https://www.econet-project.eu/Repository/DownloadFile/291 (2011)
  25. 25.
    Bolla, R., et al.: Econet deliverable d4.1 definition of energy-aware states. https://www.econet-project.eu/Repository/Document/331 (2011)
  26. 26.
    Bolla, R., Bruschi, R., Davoli, F., Gregorio, L.D., Donadio, P., Fialho, L., Collier, M., Lombardo, A., Recupero, D.R., Szemethy, T.: Green abstraction layer (GAL): power management capabilities of the future energy telecommunication fixed network nodes. Technical Report ES 203 237, ETSI, 2014Google Scholar
  27. 27.
    Bolla, R., Bruschi, R., Lago, P.: Energy adaptation in multi-core software routers. Comput. Netw. 65, 111128 (2014)Google Scholar
  28. 28.
    Bradner, S., McQuaid, J.: RFC 2544: benchmarking methodology for network interconnect devices (1999)Google Scholar
  29. 29.
    Chabarek, J., Sommers, J., Barford, P., Estan, C., Tsiang, D., Wright, S.: Power awareness in network design and routing. In: Proceedings 27th Conference on Computer Communications (INFOCOM 2008), pp. 457–465 (2008)Google Scholar
  30. 30.
    Chiaraviglio, L., Mellia, M., Neri, F.: Energy-aware backbone networks: a case study. In: Proceedings 1st International Workshop on Green Communications, IEEE International Conference on Communications (ICC’09), pp. 1–5. IEEE (2009)Google Scholar
  31. 31.
    Chiaraviglio, L., Mellia, M., Neri, F.: Minimizing ISP network energy cost: formulation and solutions. IEEE/ACM Trans. Netw. 20, 463–476 (2011)CrossRefGoogle Scholar
  32. 32.
    Choi, J., Govindan, S., Urgaonkar, B., Sivasubramaniam, A.: Profiling, prediction, and capping of power consumption in consolidated environments. In: MASCOTS 2008. IEEE International Symposium on Modeling, Analysis and Simulation of Computers and Telecommunication Systems, 2008, pp. 110, Sept 2008Google Scholar
  33. 33.
    Cianfrani, A., Eramo, V., Listani, M., Marazza, M., Vittorini, E.: An energy saving routing algorithm for a Green OSPF protocol. In: Proceedings IEEE INFOCOM Conference on Computer Communications, pp. 1–5. IEEE (2010)Google Scholar
  34. 34.
    Cisco Systems, Inc.: Cisco Data Center Infrastructure 2.5 Design Guide (2011)Google Scholar
  35. 35.
    Cuomo, F., Abbagnale, A., Cianfrani, A., Polverini, M.: Keeping the connectivity and saving the energy in the Internet. In: Proceedings IEEE INFOCOM 2011 Workshop on Green Communications and Networking, pp. 319–324. IEEE (2011)Google Scholar
  36. 36.
    DeBonis, D., Grant, R.E., Olivier, S.L., Levenhagen, M., Kelly, S.M., Pedretti, K.T., Laros, J.H.: A power api for the hpc community. Sandia Report SAND2014-17061, Sandia National Laboratories (2014)Google Scholar
  37. 37.
    Diouri, M.E.M., Dolz, M.F., Glück, O., Lefèvre, L., Alonso, P., Catalán, S., Mayo, R., Quintana-Ortí, E.S.: Assessing power monitoring approaches for energy and power analysis of computers. Sustain. Comput.: Inf. Syst. 4(2), 68–82 (2014)Google Scholar
  38. 38.
    Dolz, M.F., Heidari, M.R., Kuhn, M., Ludwig, T., Fabregat, G.: ARDUPOWER: a low-cost wattmeter to improve energy efficiency of HPC applications. In: Green Computing Conference and Sustainable Computing Conference (IGSC), 2015 Sixth International, pp. 1–8, Dec 2015Google Scholar
  39. 39.
    Dongarra, J. et al.: The international exascale software project roadmap. Int. J. High Perform. Comput. Appl. 25, 3–60 (2011)Google Scholar
  40. 40.
    Dongarra, J.J., Luszczek, P., Petitet, A.: The LINPACK benchmark: past, present and future. Concurrency Comput.: Pract. Experience 15(9):803–820 (2003)Google Scholar
  41. 41.
    SNIA Emerald: SNIA emerald power efficiency measurement specification. www.snia.org
  42. 42.
    Fisher, W., Suchara, M., Rexford, J.: Greening backbone networks: reducing energy consumption by shutting off cables in bundled links. In: Proceedings 1st ACM SIGCOMM Workshop on Green Networking (Green Networking’10), pp. 29–34. ACM (2010)Google Scholar
  43. 43.
    Floyd, M., Allen-Ware, M., Buyuktosunoglu, A., Rajamani, K., Brock, B., Lefurgy, C., Drake, A.J., Pesantez, L., Gloekler, T., Tierno, J.A., et al.: Introducing the adaptive energy management features of the Power7 chip. IEEE Micro 2, 60–75 (2011)Google Scholar
  44. 44.
    Franklin, G.F., David Powell, J., Workman, M.L.: Digital control of dynamic systems, vol. 3. Addison-Wesley Menlo Park (1998)Google Scholar
  45. 45.
    Gandhi, A., Harchol-Balter, M., Das, R., Lefurgy, C.: Optimal power allocation in server farms. In: ACM SIGMETRICS Performance Evaluation Review, vol. 37, pp. 157–168. ACM (2009)Google Scholar
  46. 46.
    Gandhi, A., Harchol-Balter, M., Ram, R., Kozuch, M.A.: Autoscale: dynamic, robust capacity management for multi-tier data centers. ACM Trans. Comput. Syst. 30(4), 14 (2012)Google Scholar
  47. 47.
    Gandhi, N., Tilbury, D.M., Diao, Y., Hellerstein, J., Parekh, S.: MIMO control of an apache web server: modeling and controller design. Proc. Am. Control Conf. 6, 4922–4927 (2002)Google Scholar
  48. 48.
    Georgiou, Y., Cadeau, T., Glesser, D., Auble, D., Jette, M., Hautreux, M.: Energy accounting and control with SLURM resource and job management system. In: Distributed Computing and Networking, pp. 96–118. Springer (2014)Google Scholar
  49. 49.
    Gerndt, M., César, E., Benkner, S. (eds.): Automatic Tuning of HPC Applications. Shaker Verlag (2015)Google Scholar
  50. 50.
    Gu, C., Heng, H., Xiuping, J.: Power metering for virtual machine in cloud computing-challenges and opportunities. IEEE Access 2, 1106–1116 (2014)Google Scholar
  51. 51.
    Hackenberg, D., Ilsche, T., Schone, R., Molka, D., Schmidt, M., Nagel, W.E.: Power measurement techniques on standard compute nodes: a quantitative comparison. In: IEEE International Symposium on Performance Analysis of Systems and Software, pp. 194–204. IEEE (2013)Google Scholar
  52. 52.
    Hackenberg, D., Ilsche, T., Schuchart, J., Schone, R., Nagel, W.E., Simon, M., Georgiou, Y.: HDEEM: high definition energy efficiency monitoring. In: Energy Efficient Supercomputing Workshop, pp. 1–10. IEEE (2014)Google Scholar
  53. 53.
    Hays, R.: Active/Idle toggling with low-power idle. Presentation at IEEE802.3az Task Force Group Meeting. http://www.ieee802.org/3/az/public/jan08/hays_01_0108.pdf (2008)
  54. 54.
    Hewlett-Packard Corp., Intel Corp., Microsoft Corp., Phoenix Technologies Ltd., and Toshiba Corp.: Advanced Configuration and Power Interface Specification, Revision 5.0 (2011)Google Scholar
  55. 55.
    Howard, J., Dighe, S., Vangal, S.R., Ruhl, G., Borkar, N., Jain, S., Erraguntla, V., Konow, M., Riepen, M., Gries, M., et al.: A 48-core IA-32 processor in 45 nm CMOS using on-die message-passing and DVFS for performance and power scaling. IEEE J. Solid-State Circuits 46(1), 173–183 (2011)Google Scholar
  56. 56.
    Idzikowski, F., Orlowski, S., Raack, Ch., Rasner, H., Wolisz, A.: Saving energy in IP-over-WDM networks by switching off line cards in low-demand scenarios. In: Proceedings 14th Conference on Optical Network Design and Modeling (ONDM’10). IEEE (2010)Google Scholar
  57. 57.
    IEEE, Institute of Electrical and Electronics Engineers, IEEE 802.3az Energy Efficient Ethernet Task Force. http://grouper.ieee.org/groups/802/3/az/public/index.html (2012)
  58. 58.
    Ilsche, T., Hackenberg, D., Graul, S., Schöne, R., Schuchart, J.: Power measurements for compute nodes: improving sampling rates, granularity and accuracy. In: Sixth International Green and Sustainable Computing Conference (2015)Google Scholar
  59. 59.
    Intel. Intel Intelligent Power Node Manager. www.intel.com
  60. 60.
    Intel Corp.: Intel 64 and IA-32 Architectures Software Developers Manual Combined Volumes: 1, 2A, 2B, 2C, 3A, 3B and 3C (2015)Google Scholar
  61. 61.
    Iosup, A., Ostermann, S., Yigitbasi, M.N., Prodan, R., Fahringer, T., Epema, D.H.J.: Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans. Parallel Distrib. Syst. 22(6), 931–945 (2011)Google Scholar
  62. 62.
    Jaskóła, P., Arabas, P., Karbowski, A.: Combined calculation of optimal routing and bandwidth allocation in energy aware networks. In: Proceedings 26th International Teletraffic Congress, Karlskrona, pp. 1–6. IEEE (2014)Google Scholar
  63. 63.
    Jaskóła, P., Arabas, P., Karbowski, A.: Simultaneous routing and flow rate optimization in energy-aware computer networks. Int. J. Appl. Math. Comput. Sci. 26(1), 231–243 (2016)MathSciNetzbMATHGoogle Scholar
  64. 64.
    Jaskóła, P., Malinowski, K.: Two methods of optimal bandwidth allocation in TCP/IP networks with QoS differentiation. In: Proceedings Summer Simulation Multiconference (SPECTS’04), pp. 373–378 (2004)Google Scholar
  65. 65.
    Jha, S., Qiu, J., Luckow, A., Mantha, P., Fox, G.C.: A tale of two data-intensive paradigms: applications, abstractions, and architectures. In: IEEE International Congress on Big Data, pp. 645–652. IEEE (2014)Google Scholar
  66. 66.
    Jing, S.-Y., Ali, S., She, K., Zhong, Y.: State-of-the-art research study for green cloud computing. J. Supercomput. 65(1), 445–468 (2013)Google Scholar
  67. 67.
    Jung, H., Pedram, M.: Supervised learning based power management for multicore processors. IEEE Trans. Comput.-Aid. Des. Integrat. Circuits Syst. 29(9), 1395–1408 (2010)Google Scholar
  68. 68.
    Melanie, K., Martha, A.K.: An experimental survey of energy management across the stack. In: Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications, pp. 329–344. ACM (2014)Google Scholar
  69. 69.
    Kamola, M., Arabas, P.: Shortest path green routing and the importance of traffic matrix knowledge. In: 2013 24th Tyrrhenian International Workshop on Digital Communications - Green ICT (TIWDC), pp. 1–6, Sept 2013Google Scholar
  70. 70.
    Karbowski, A., Jaskóła, P.: Two approaches to dynamic power management in energy-aware computer networks - methodological considerations. In: Proceedings of Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1177–1182, Sept 2015Google Scholar
  71. 71.
    Karpowicz, M., Arabas, P.: Energy-aware multi-level control system for a network of linux software routers: design and implementation. IEEE Syst. J. PP(99):1–12 (2015)Google Scholar
  72. 72.
    Karpowicz, M.P.: Energy-efficient CPU frequency control for the Linux system. Concurrency Comput.: Pract. Experience 28(2):420–437 (2016). cpe.3476Google Scholar
  73. 73.
    Karpowicz, M.P., Arabas, P.: Preliminary results on the Linux libpcap model identification. In: 20th International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 1056–1061. IEEE (2015)Google Scholar
  74. 74.
    Kołodziej, J., Khan, S.U., Wang, L., Zomaya, A.Y.: Energy efficient genetic-based schedulers in computational grids. Concurrency Comput.: Pract. Experience (2012). doi: 10.1002/cpe.2839
  75. 75.
    Kondo, M., Sasaki, H., Nakamura, H.: Improving fairness, throughput and energy-efficiency on a chip multiprocessor through DVFS. ACM SIGARCH Comput. Architect. News 35(1), 31–38 (2007)Google Scholar
  76. 76.
    Koomey, J.: Growth in data center electricity use 2005 to 2010. Analytics Press, Oakland, Aug, 1, 2010, 2011Google Scholar
  77. 77.
    Lange, K.-D.: Identifying shades of green: the SPECpower benchmarks. IEEE Comput. 42(3), 95–97 (2009)Google Scholar
  78. 78.
    Laros, J.H., III, DeBonis, D., Grant, R., Kelly, S.M., Levenhagen, M., Olivier, S., Pedretti, K.: High performance computing-power application programming interface specification. Technical Report SAND2014-17061, Sandia National Laboratories (2014)Google Scholar
  79. 79.
    Lefurgy, C., Rajamani, K., Rawson, F., Felter, W., Kistler, M., Keller, T.W.: Energy management for commercial servers. Computer 36(12), 39–48 (2003)CrossRefGoogle Scholar
  80. 80.
    Lefurgy, C., Wang, X., Ware, M.: Server-level power control. In: The 4th IEEE International Conference on Autonomic Computing. IEEE (2007)Google Scholar
  81. 81.
    Lefurgy, C., Wang, X., Ware, M.: Power capping: a prelude to power shifting. Cluster Comput. 11(2), 183–195 (2008)Google Scholar
  82. 82.
    Lefurgy, C.R., Drake, A.J., Floyd, M.S., Allen-Ware, M.S., Brock, B., Tierno, J.A., Carter, J.B., Berry, R.W.: Active guardband management in Power7+ to save energy and maintain reliability. IEEE Micro 33(4), 35–45 (2013)Google Scholar
  83. 83.
    Lim, H., Kansal, A., Liu, J.: Power budgeting for virtualized data centers. In: 2011 USENIX Annual Technical Conference (USENIX ATC’11), p. 59 (2011)Google Scholar
  84. 84.
    Ljung, L.: System Identification. Prentice Hall, Upper Saddle River (1998)Google Scholar
  85. 85.
    Lorch, J.R., Smith, A.J.: Improving dynamic voltage scaling algorithms with pace. In: Proceedings ACM SIGMETRICS 2001 International Conference on Measurement and Modeling of Computer Systems, p. 5061 (2001)Google Scholar
  86. 86.
    Lu, Z., Hein, J., Humphrey, M., Stan, M., Lach, J., Skadron, K.: Control-theoretic dynamic frequency and voltage scaling for multimedia workloads. In: Proceedings of the 2002 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems, pp. 156–163. ACM (2002)Google Scholar
  87. 87.
    Mair, J., Eyers, D., Huang, Z., Zhang, H.: Myths in power estimation with performance monitoring counters. Sustain. Comput.: Inf. Syst. 4(2), 83–93 (2014)Google Scholar
  88. 88.
    Malinowski, K., Niewiadomska-Szynkiewicz, E., Jaskóła, P.: Price method and network congestion control. J. Telecommun. Inf. Technol. 2, 73–77 (2010)Google Scholar
  89. 89.
    Mastelic, T., Oleksiak, A., Claussen, H., Brandic, I., Pierson, J.-M., Vasilakos, A.V.: Cloud computing: survey on energy efficiency. ACM Comput. Surv. 47(2):33 (2015)Google Scholar
  90. 90.
    McCullough, J.C., Agarwal, Y., Chandrashekar, J., Kuppuswamy, S., Snoeren, A.C., Gupta, R.K.: Evaluating the effectiveness of model-based power characterization. In: USENIX Annual Technical Conference (2011)Google Scholar
  91. 91.
    Min, R., Furrer, T., Chandrakasan, A.: Dynamic voltage scaling techniques for distributed microsensor networks. In: Proceedings IEEE Workshop on VLSI, pp. 43–46 (2000)Google Scholar
  92. 92.
    Mobius, C., Dargie, W., Schill, A.: Power consumption estimation models for processors, virtual machines, and servers. IEEE Trans. Parallel Distrib. Syst. 25(6), 1600–1614 (2014)Google Scholar
  93. 93.
    Molka, D., Hackenberg, D., Schöne, R., Minartz, T., Nagel, W.E.: Flexible workload generation for HPC cluster efficiency benchmarking. Comput. Sci.-Res. Dev. 27(4):235–243 (2012)Google Scholar
  94. 94.
    Nakai, M., Akui, S., Seno, K., Meguro, T., Seki, T., Kondo, T., Hashiguchi, A., Kawahara, H., Kumano, K., Shimura, M.: Dynamic voltage and frequency management for a low-power embedded microprocessor. IEEE J. Solid-State Circuits 40(1), 28–35 (2005)Google Scholar
  95. 95.
    Naveh, A., Rajwan, D., Ananthakrishnan, A., Weissmann, E.: Power management architecture of the 2nd generation Intel Core microarchitecture, formerly codenamed Sandy Bridge. In: Hot Chips, vol. 23, p. 0 (2011)Google Scholar
  96. 96.
    Niewiadomska-Szynkiewicz, E., Sikora, A., Arabas, P., Kamola, M., Mincer, M., Koodziej, J.: Dynamic power management in energy-aware computer networks and data intensive systems. Future Gener. Comput. Syst. 37, 284–296 (2014)CrossRefGoogle Scholar
  97. 97.
    Niewiadomska-Szynkiewicz, E., Sikora, A., Arabas, P., Kołodziej, J.: Control framework for high performance energy aware backbone network. In: Proceedings of European Conference on Modelling and Simulation (ECMS 2012), pp. 490–496 (2012)Google Scholar
  98. 98.
    Niewiadomska-Szynkiewicz, E., Sikora, A., Arabas, P., Kołodziej, J.: Control system for reducing energy consumption in backbone computer network. Concurrency Comput.: Pract. Experience 25, 1738–1754 (2013)CrossRefGoogle Scholar
  99. 99.
    NVIDIA. NVML API Reference Manual. www.developer.nvidia.com (2012)
  100. 100.
    Padala, P., Hou, K.-Y., Shin, K.G., Zhu, X., Uysal, M., Wang, Z., Singhal, S., Merchant, A.: Automated control of multiple virtualized resources. In: Proceedings of the 4th ACM European Conference on Computer Systems, pp. 13–26. ACM (2009)Google Scholar
  101. 101.
    Pallipadi, V., Li, S., Belay, A.: cpuidle: do nothing, efficiently. Proc. Linux Symp. 2, 119–125 (2007)Google Scholar
  102. 102.
    Pallipadi, V., Starikovskiy, A.: The ondemand governor. Proc. Linux Symp. 2, 215–230 (2006)Google Scholar
  103. 103.
    Patikirikorala, T., Colman, A., Han, J., Wang, L.: A systematic survey on the design of self-adaptive software systems using control engineering approaches. In: ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), pp. 33–42. IEEE (2012)Google Scholar
  104. 104.
    Pióro, M., Mysłek, M., Juttner, A., Harmatos, J., Szentesi, A.: Topological design of MPLS networks. In: Proceedings GLOBECOM’2001 (2001)Google Scholar
  105. 105.
    Qureshi, A., Weber, R., Balakrishnan, H.: Cutting the electric bill for internet-scale systems. In: SIGCOMM’09, pp. 123–134. ACM, Aug 17–21 2009Google Scholar
  106. 106.
    Restrepo, J., Gruber, C., Machuca, C.: Energy profile aware routing. In: Proceedings 1st International Workshop on Green Communications, IEEE International Conference on Communications (ICC’09), pp. 1–5 (2009)Google Scholar
  107. 107.
    Roy, S.N.: Energy logic: a road map to reducing energy consumption in telecom munications networks. In: Proceedings 30th International Telecommunication Energy Conference (INTELEC 2008) (2008)Google Scholar
  108. 108.
    Sikora, A., Niewiadomska-Szynkiewicz, E.: A federated approach to parallel and distributed simulation of complex systems. Appl. Math. Comput. Sci. 17(1), 99–106 (2007)Google Scholar
  109. 109.
    Storage Performance Council (SPC): Storage Performance Council SPC Benchmark 2/Energy Extension. www.storageperformance.org
  110. 110.
    Standard Performance Evaluation Corporation (SPEC): SPEC Power and Performance Benchmark Methodology. www.spec.org/power_ssj2008/
  111. 111.
    Spiliopoulos, V., Kaxiras, S., Keramidas, G.: Green governors: a framework for continuously adaptive DVFS. In: 2011 International Green Computing Conference and Workshops (IGCC), pp. 1–8. IEEE (2011)Google Scholar
  112. 112.
    Subramaniam, B., Feng, W.: Towards energy-proportional computing for enterprise-class server workloads. In: Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering, pp. 15–26. ACM (2013)Google Scholar
  113. 113.
    Subramaniam, B., Saunders, W., Scogland, T., Feng, W.: Trends in energy-efficient computing: a perspective from the Green500. In: 2013 International Green Computing Conference (IGCC), pp. 1–8. IEEE (2013)Google Scholar
  114. 114.
    Taniça, L., Ilic, A., Tomás, P., Sousa, L.: Schedmon: a performance and energy monitoring tool for modern multi-cores. In: Euro-Par 2014: Parallel Processing Workshops, pp. 230–241. Springer (2014)Google Scholar
  115. 115.
    Valentini, G.L., Lassonde, W., Khan, S.U., Min-Allah, N., Madani, S.A., Li, J., Zhang, L., Wang, L., Ghani, N., Kołodziej, J., Li, H., Zomaya, A.Y., Xu, C.-Z., Balaji, P., Vishnu, A., Pinel, F., Pecero, J.E., Kliazovich, D., Bouvry, P.: An overview of energy efficiency techniques in cluster computing systems. Cluster Comput. (2011). doi: 10.1007/s10586-011-0171-x
  116. 116.
    Vasić, N., Kostić, D.: Energy-aware traffic engineering. In: Proceedings 1st International Conference on Energy-Efficient Computing and Networking (E-ENERGY 2010) (2010)Google Scholar
  117. 117.
    Vavilapalli, V.K., Murthy, A.C., Douglas, C., Agarwal, S., Konar, M., Evans, R., Graves, T., Lowe, J., Shah, H., Seth, S., et al.: Apache hadoop yarn: yet another resource negotiator. In Proceedings of the 4th Annual Symposium on Cloud Computing, p. 5. ACM (2013)Google Scholar
  118. 118.
    Wang, L., Khan, S.U.: Review of performance metrics for green data centers: a taxonomy study. J. Supercomput. (2011). doi: 10.1007/s11227-011-0704-3:1-18
  119. 119.
    Wang, L., Khan, S.U.: Review of performance metrics for green data centers: a taxonomy study. J. Supercomput. 63(3), 639–656 (2013)Google Scholar
  120. 120.
    Wang, X., Wang, Y.: Coordinating power control and performance management for virtualized server clusters. IEEE Trans. Parallel Distrib. Syst. 22(2), 245–259 (2011)Google Scholar
  121. 121.
    Wang, Y., Wang, X., Chen, M., Zhu, X.: Partic: power-aware response time control for virtualized web servers. IEEE Trans. Parallel Distrib. Syst. 22(2), 323–336 (2011)Google Scholar
  122. 122.
    Weaver, V.M., Johnson, M., Kasichayanula, K., Ralph, J., Luszczek, P., Terpstra, D., Moore, S.: Measuring energy and power with PAPI. In: 41st International Conference on Parallel Processing Workshops (ICPPW), 2012, pp. 262–268. IEEE (2012)Google Scholar
  123. 123.
    Wu, B., Li, P.: Load-aware stochastic feedback control for DVFS with tight performance guarantee. In: 2012 IEEE/IFIP 20th International Conference on VLSI and System-on-Chip (VLSI-SoC), pp. 231–236, Oct 2012Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Michał Karpowicz
    • 1
    • 2
  • Ewa Niewiadomska-Szynkiewicz
    • 1
    • 2
    Email author
  • Piotr Arabas
    • 1
    • 2
  • Andrzej Sikora
    • 2
  1. 1.Warsaw University of TechnologyWarsawPoland
  2. 2.Research and Academic Computer Network (NASK)WarsawPoland

Personalised recommendations