Skip to main content
SpringerLink
Log in

Techniques to Achieve Energy Proportionality in Data Centers: A Survey

  • Chapter
  • First Online:
Handbook on Data Centers

Abstract

A data center is a set of physical and possibly virtual machines along with other components such as storage, network, cooling, power supplies and management software, that function together to serve data and information to facilitate information services to a business or organization. It consists of computing and data dissemination as the main functions, however there are several other physical elements such as cooling management and power budgeting that interact with the computing elements, thusly making a data center to exhibit both a cyber and a physical behavior.

This work has been funded in parts by NSF grant CSR #1218505 and CRI #0855277

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. L. A. Barroso and U. Hölzle, “The case for energy-proportional computing,” Computer, vol. 40, no. 12, pp. 33–37, Dec 2007.

    Article  Google Scholar 

  2. P. Ranganathan, P. Leech, D. Irwin, and J. Chase, “Ensemble-level power management for dense blade servers,” SIGARCH Comput. Archit. News, vol. 34, pp. 66–77, May 2006. [Online]. Available: http://doi.acm.org/10.1145/1150019.1136492.

    Article  Google Scholar 

  3. T. Starner, “Human-powered wearable computing,” IBM Systems Journal, vol. 35, no. 3.4, pp. 618–629, 1996.

    Article  Google Scholar 

  4. G. Varsamopoulos, Z. Abbasi, and S. K. S. Gupta, “Trends and effects of energy proportionality on server provisioning in data centers,” in International Conference on High performance Computing (HiPC2010), Goa, India, Dec 2010.

    Google Scholar 

  5. X. Feng, R. Ge, and K. W. Cameron, “Power and energy profiling of scientific applications on distributed systems,” in Proceedings. 19th IEEE International Parallel and Distributed Processing Symposium, 2005. IEEE, 2005, p. 34.

    Google Scholar 

  6. D. Tsirogiannis, S. Harizopoulos, and M. A. Shah, “Analyzing the energy efficiency of a database server,” in Proceedings of the 2010 international conference on Management of data SIGMOD'10. ACM, 2010, pp. 231–242.

    Google Scholar 

  7. “Standard performance evaluation corporation specweb 2009.”

    Google Scholar 

  8. G. Varsamopoulos and S. K. Gupta, “Energy proportionality and the future: Metrics and directions,” in Proceedings of the 2010 39th International Conference on Parallel Processing Workshops, ser. ICPPW'10. Washington, DC, USA: IEEE Computer Society, 2010, pp. 461–467. [Online]. Available: http://dx.doi.org/10.1109/ICPPW.2010.68.

  9. B.-G. Chun, G. Iannaccone, G. Iannaccone, R. Katz, G. Lee, and L. Niccolini, “An energy case for hybrid datacenters,” ACM SIGOPS Operating Systems Review, vol. 44, no. 1, pp. 76–80, 2010.

    Google Scholar 

  10. R. Raghavendra, P. Ranganathan, V. Talwar, Z. Wang, and X. Zhu, “No “power” struggles: coordinated multi-level power management for the data center,” SIGARCH Comput. Archit. News, vol. 36, pp. 48–59, March 2008. [Online]. Available: http://doi.acm.org/10.1145/1353534.1346289.

  11. T. Mukherjee, A. Banerjee, G. Varsamopoulos, S. K. S. Gupta, and S. Rungta, “Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers,” Computer Networks, June 2009. [Online]. Available: http://dx.doi.org/10.1016/j.comnet.2009.06.008.

  12. Z. Abbasi, G. Varsamopoulos, and S. K. S. Gupta, “Thermal aware server provisioning and workload distribution for internet data centers,” in ACM International Symposium on High Performance Distributed Computing (HPDC10), Chicago, IL, June 2010.

    Google Scholar 

  13. C. Bash and G. Forman, “Cool job allocation: Measuring the power savings of placing jobs at cooling-efficient locations in the data center,” HP Laboratories Palo Alto, Tech. Rep. HPL-2007-62, August 2007.

    Google Scholar 

  14. X. Fan, W.-D. Weber, and L. A. Barroso, “Power provisioning for a warehouse-sized computer,” in Proceedings of the 34th annual international symposium on Computer architecture. ACM, 2007, pp. 13–23.

    Google Scholar 

  15. J. Racino, “PUE.” [Online]. Available: http://www.thegreengrid.org.

  16. “Environmental protection agency, energy star program, report to congress on server and data energy efficiency,” 2007. [Online]. Available: http://www.energystar.gov/ia/partners/prod_development/downloads/EPA_Datacenter_Report_Congress_Final1.pdf.

  17. A. Gandhi, M. Harchol-Balter, R. Das, and C. Lefurgy, “Optimal power allocation in server farms,” in Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems, ser. SIGMETRICS '09. New York, NY, USA: ACM, 2009, pp. 157–168. [Online]. Available: http://doi.acm.org/10.1145/1555349.1555368.

  18. R. Ge, X. Feng, W. Chun Feng, and K. Cameron, “CPU MISER: A performance-directed, run-time system for power-aware clusters,” in International Conference on Parallel Processing, 2007. ICPP 2007, Sept. 2007, p. 18.

    Google Scholar 

  19. D. Meisner, B. T. Gold, and T. F. Wenisch, “The powernap server architecture,” ACM Trans. Comput. Syst., vol. 29, pp. 3:1–3:24, February 2011. [Online]. Available: http://doi.acm.org/10.1145/1925109.1925112.

  20. J. E. Moreira and J. P. Karidis, “The case for full-throttle computing: An alternative datacenter design strategy,” Micro, IEEE, vol. 30, no. 4, pp. 25–28, July–Aug 2010.

    Google Scholar 

  21. Q. Deng, D. Meisner, L. Ramos, T. F. Wenisch, and R. Bianchini, “Memscale: active low-power modes for main memory,” in Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems, ser. ASPLOS'11. New York, NY, USA: ACM, 2011, pp. 225–238. [Online]. Available: http://doi.acm.org/10.1145/1950365.1950392.

  22. J. H. Ahn, N. P. Jouppi, C. Kozyrakis, J. Leverich, and R. S. Schreiber, “Future scaling of processor-memory interfaces,” in Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, ser. SC'09. New York, NY, USA: ACM, 2009, pp. 42:1–42:12. [Online]. Available: http://doi.acm.org/10.1145/1654059.1654102.

  23. H. Zheng, J. Lin, Z. Zhang, E. Gorbatov, H. David, and Z. Zhu, “Mini-rank: Adaptive dram architecture for improving memory power efficiency,” in Proceedings of the 41st annual IEEE/ACM International Symposium on Microarchitecture, ser. MICRO 41. Washington, DC, USA: IEEE Computer Society, 2008, pp. 210–221. [Online]. Available: http://dx.doi.org/10.1109/MICRO.2008.4771792.

  24. S. Nedevschi, L. Popa, G. Iannaccone, S. Ratnasamy, and D. Wetherall, “Reducing network energy consumption via sleeping and rate-adaptation,” in Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, ser. NSDI'08. Berkeley, CA, USA: USENIX Association, 2008, pp. 323–336. [Online]. Available: http://dl.acm.org/citation.cfm?id=1387589.1387612.

  25. M. Gupta and S. Singh, “Using low-power modes for energy conservation in ethernet lans,” in INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE, may 2007, pp. 2451–2455.

    Google Scholar 

  26. G. Ananthanarayanan and R. H. Katz, “Greening the switch,” in Proceedings of the 2008 conference on Power aware computing and systems, ser. HotPower'08. Berkeley, CA, USA: USENIX Association, 2008, pp. 7–7. [Online]. Available: http://portal.acm.org/citation.cfm?id=1855610.1855617.

  27. B. Heller, S. Seetharaman, P. Mahadevan, Y. Yiakoumis, P. Sharma, S. Banerjee, and N. McKeown, “Elastictree: saving energy in data center networks,” in Proceedings of the 7th USENIX conference on Networked systems design and implementation, ser. NSDI'10. Berkeley, CA, USA: USENIX Association, 2010, pp. 17–17. [Online]. Available: http://portal.acm.org/citation.cfm?id=1855711.1855728.

  28. M. Gupta and S. Singh, “Greening of the internet,” in Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications, ser. SIGCOMM'03. New York, NY, USA: ACM, 2003, pp. 19–26. [Online]. Available: http://doi.acm.org/10.1145/863955.863959.

  29. N. Tolia, Z. Wang, M. Marwah, C. Bash, P. Ranganathan, and X. Zhu, “Delivering energy proportionality with non energy-proportional systems: optimizing the ensemble,” in Proceedings of the 2008 conference on Power aware computing and systems. USENIX Association, 2008, pp. 2–2.

    Google Scholar 

  30. D. Meisner, B. T. Gold, and T. F. Wenisch, “Powernap: eliminating server idle power,” SIGPLAN Notices, vol. 44, pp. 205–216, March 2009. [Online]. Available: http://doi.acm.org/10.1145/1508284.1508269.

  31. G. Chen, W. He, J. Liu, S. Nath, L. Rigas, L. Xiao, and F. Zhao, “Energy-aware server provisioning and load dispatching for connection-intensive internet services,” in NSDI'08: Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation. Berkeley, CA, USA: USENIX Association, 2008, pp. 337–350.

    Google Scholar 

  32. D. Kusic, J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang, “Power and performance management of virtualized computing environments via lookahead control,” Cluster Computing, vol. 12, pp. 1–15, 2009.

    Article  Google Scholar 

  33. J. Chase, D. Anderson, P. Thakar, A. Vahdat, and R. Doyle, “Managing energy and server resources in hosting centers,” in SOSP'01: Proceedings of the eighteenth ACM symposium on Operating systems principles. New York, NY, USA: ACM, 2001, pp. 103–116.

    Google Scholar 

  34. A. Faraz and T. Vijaykumar, “Joint optimization of idle and cooling power in data centers while maintaining response time,” ACM SIGARCH Computer Architecture News, vol. 38, no. 1, pp. 243–256, 2010.

    Google Scholar 

  35. B. Guenter, N. Jain, and C. Williams, “Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning,” in Proc. IEEE INFOCOM, Shanghai, China. IEEE, 2011, pp. 702–710.

    Google Scholar 

  36. A. Krioukov, P. Mohan, S. Alspaugh, L. Keys, D. Culler, and R. Katz, “Napsac: design and implementation of a power-proportional web cluster,” in Proceedings of the first ACM SIGCOMM workshop on Green networking. ACM, 2010, pp. 15–22.

    Google Scholar 

  37. M. Lin, A. Wierman, L. L. H. Andrew, and E. Thereska, “Dynamic right-sizing for power-proportional data centers,” in Proc. IEEE INFOCOM, Shanghai, China, 2011, pp. 10–15.

    Google Scholar 

  38. F. Hermenier, X. Lorca, J. M. Menaud, G. Muller, and J. Lawall, “Entropy: a consolidation manager for clusters,” in ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environment, Washington, DC, USA, March 2009, pp. 41–50.

    Google Scholar 

  39. S.-H. Lim, J.-S. Huh, Y. Kim, and C. R. Das, “Migration, assignment, and scheduling of jobs in virtualized environment,” in HotCloud, June, 2011.

    Google Scholar 

  40. G. Dhiman, G. Marchetti, and T. Rosing, “vgreen: A system for energy-efficient management of virtual machines,” ACM Trans. Des. Autom. Electron. Syst., vol. 16, pp. 6:1–6:27, November 2010. [Online]. Available: http://doi.acm.org/10.1145/1870109.1870115.

  41. D. Ardagna, B. Panicucci, M. Trubian, and L. Zhang, “Energy-aware autonomic resource allocation in multi-tier virtualized environments,” IEEE Transactions on Services Computing, vol. 99, no. PrePrints, 2010.

    Google Scholar 

  42. L. Lu, P. J. Varman, and K. Doshi, “Decomposing workload bursts for efficient storage resource management,” IEEE Transactions on Parallel and Distributed Systems, pp. 860–873, 2010.

    Google Scholar 

  43. R. K. Sharma, C. E. Bash, C. D. Patel, R. J. Friedrich, and J. S. Chase, “Balance of power: Dynamic thermal management for internet data centers,” IEEE Internet Computing, pp. 42–49, 2005.

    Google Scholar 

  44. V. Vasudevan, D. Andersen, M. Kaminsky, L. Tan, J. Franklin, and I. Moraru, “Energy-efficient cluster computing with fawn: workloads and implications,” in Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking, ser. e-Energy'10. New York, NY, USA: ACM, 2010, pp. 195–204. [Online]. Available: http://doi.acm.org/10.1145/1791314.1791347.

  45. K. Lim, P. Ranganathan, J. Chang, C. Patel, T. Mudge, and S. Reinhardt, “Understanding and designing new server architectures for emerging warehouse-computing environments,” SIGARCH Comput. Archit. News, vol. 36, pp. 315–326, June 2008. [Online]. Available: http://doi.acm.org/10.1145/1394608.1382148.

  46. 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 Proceedings. IEEE, 2010, pp. 1–9.

    Google Scholar 

  47. K. Ley, R. Bianchiniy, M. Martonosiz, and T. D. Nguyeny, “Cost and energy aware load distribution across data centers,” in SOSP Workshop on Power Aware Computing and Systems(HotPower'09), 2009.

    Google Scholar 

  48. Z. Abbasi, T. Mukherjee, G. Varsamopoulos, and S. K. S. Gupta, “Dynamic hosting management of web based applications over clouds,” in International Conference on High performance Computing (HiPC2011), India, dec 2011.

    Google Scholar 

  49. A. Qureshi, R. Weber, H. Balakrishnan, J. Guttag, and B. Maggs, “Cutting the electric bill for internet-scale systems,” in Proceedings of the ACM SIGCOMM 2009 conference on Data communication. ACM, 2009, pp. 123–134.

    Google Scholar 

  50. S. Govindan, A. Sivasubramaniam, and B. Urgaonkar, “Benefits and limitations of tapping into stored energy for datacenters,” in Proc. The 38th International Symposium on Computer Architecture (ISCA), San Jose, CA, USA, June 2011.

    Google Scholar 

  51. R. Urgaonkar, B. Urgaonkar, M. J. Neely, and A. Sivasubramaniam, “Optimal power cost management using stored energy in data centers,” Arxiv preprint arXiv:1103.3099, 2011.

    Google Scholar 

  52. N. Buchbinder, N. Jain, and I. Menache, “Online job-migration for reducing the electricity bill in the cloud,” NETWORKING 2011, pp. 172–185, 2011.

    Google Scholar 

  53. P. Ranganathan, “Recipe for efficiency: Principles of power-aware computing,” in Commun. ACM, vol. 53, no. 4. New York, NY, USA: ACM, Apr. 2010, pp. 60–67. [Online]. Available: http://doi.acm.org/10.1145/1721654.1721673.

  54. X. Fan, W.-D. Weber, and L. A. Barroso, “Power provisioning for a warehouse-sized compute r,” SIGARCH Comput. Archit. News, vol. 35, pp. 13–23, June 2007. [Online]. Available: http://doi.acm.org/10.1145/1273440.1250665.

  55. D. Wei, “ACPI advanced configuration and power interface,” March 2013. [Online]. Available: URL.

    Google Scholar 

  56. S. Zhuravlev, J. C. Saez, S. Blagodurov, A. Fedorova, and M. Prieto, “Survey of energy-cognizant scheduling techniques,” IEEE Transactions on Parallel and Distributed Systems, vol. 99, no. PrePrints, 2012.

    Google Scholar 

  57. L. L. Andrew, M. Lin, and A. Wierman, “Optimality, fairness, and robustness in speed scaling designs,” in Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems, ser. SIGMETRICS'10. New York, NY, USA: ACM, 2010, pp. 37–48. [Online]. Available: http://doi.acm.org/10.1145/1811039.1811044.

  58. A. Wierman, L. Andrew, and A. Tang, “Power-aware speed scaling in processor sharing systems,” in INFOCOM 2009, IEEE, April 2009, pp. 2007–2015.

    Google Scholar 

  59. R. Ge, X. Feng, W. chun Feng, and K. Cameron, “Cpu miser: A performance-directed, run-time system for power-aware clusters,” in Parallel Processing, 2007. ICPP 2007. International Conference on, Sept. 2007, p. 18.

    Google Scholar 

  60. G. Dhiman and T. S. Rosing, “Dynamic voltage frequency scaling for multi-tasking systems using online learning,” in Proceedings of the 2007 international symposium on Low power electronics and design, ser. ISLPED'07. New York, NY, USA: ACM, 2007, pp. 207–212. [Online]. Available: http://doi.acm.org/10.1145/1283780.1283825.

  61. M. Ghasemazar, E. Pakbaznia, and M. Pedram, “Minimizing energy consumption of a chip multiprocessor through simultaneous core consolidation and DVFS,” in Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), 30 2010-June 2 2010, pp. 49–52.

    Google Scholar 

  62. P. Bailis, V. Reddi, S. Gandhi, D. Brooks, and M. Seltzer, “Dimetrodon: Processor-level preventive thermal management via idle cycle injection,” in Design Automation Conference (DAC), 2011 48th ACM/EDAC/IEEE, June 2011, pp. 89–94.

    Google Scholar 

  63. K. Kang, J. Kim, S. Yoo, and C.-M. Kyung, “Temperature-aware integrated DVFS and power gating for executing tasks with runtime distribution,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 29, no. 9, pp. 1381–1394, Sept. 2010.

    Google Scholar 

  64. J. Leverich, M. Monchiero, V. Talwar, P. Ranganathan, and C. Kozyrakis, “Power management of datacenter workloads using per-core power gating,” IEEE Comput. Archit. Lett., vol. 8, no. 2, pp. 48–51, July 2009. [Online]. Available: http://dx.doi.org/10.1109/L-CA.2009.46.

  65. X. Wang and Y. Wang, “Coordinating power control and performance management for virtualized server clusters,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, pp. 245–259, 2011.

    Article  Google Scholar 

  66. R. Ayoub, U. Ogras, E. Gorbatov, Y. Jin, T. Kam, P. Diefenbaugh, and T. Rosing, “OS-level power minimization under tight performance constraints in general purpose systems,” in International Symposium on Low Power Electronics and Design (ISLPED) 2011, Aug. 2011, pp. 321–326.

    Google Scholar 

  67. S. Cho and R. G. Melhem, “Corollaries to Amdahl’s law for energy,” Computer Architecture Letters, vol. 7, no. 1, pp. 25–28, 2008.

    Google Scholar 

  68. S. Herbert and D. Marculescu, “Analysis of dynamic voltage/frequency scaling in chip-multiprocessors,” in ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), 2007, Aug. 2007, pp. 38–43.

    Google Scholar 

  69. S. Zhuravlev, J. C. Saez, S. Blagodurov, A. Fedorova, and M. Prieto, “Survey of energy-cognizant scheduling techniques,” IEEE Transactions on Parallel and Distributed Systems, vol. 99, no. PrePrints, 2012.

    Google Scholar 

  70. X. Fan, C. Ellis, and A. Lebeck, “Memory controller policies for dram power management,” in Proceedings of the 2001 international symposium on Low power electronics and design, ser. ISLPED'01. New York, NY, USA: ACM, 2001, pp. 129–134. [Online]. Available: http://doi.acm.org/10.1145/383082.383118.

  71. H. Huang, P. Pillai, and K. G. Shin, “Design and implementation of power-aware virtual memory,” in Proceedings of the annual conference on USENIX Annual Technical Conference. Berkeley, CA, USA: USENIX Association, 2003, pp. 5–5. [Online]. Available: http://portal.acm.org/citation.cfm?id=1247340.1247345.

  72. X. Li, Z. Li, F. David, P. Zhou, Y. Zhou, S. Adve, and S. Kumar, “Performance directed energy management for main memory and disks,” SIGARCH Comput. Archit. News, vol. 32, pp. 271–283, October 2004. [Online]. Available: http://doi.acm.org/10.1145/1037947.1024425.

  73. J. H. Ahn, N. P. Jouppi, C. Kozyrakis, J. Leverich, and R. S. Schreiber, “Future scaling of processor-memory interfaces,” in Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, ser. SC'09. New York, NY, USA: ACM, 2009, pp. 42:1–42:12. [Online]. Available: http://doi.acm.org/10.1145/1654059.1654102.

  74. Q. Zou, “An analytical performance and power model based on the transition probability for hard disks,” in 3rd International Conference on Awareness Science and Technology (iCAST), 2011, Sept. 2011, pp. 111–116.

    Google Scholar 

  75. A. Verma, R. Koller, L. Useche, and R. Rangaswami, “Srcmap: energy proportional storage using dynamic consolidation,” in Proceedings of the 8th USENIX conference on File and storage technologies, ser. FAST'10. Berkeley, CA, USA: USENIX Association, 2010, pp. 20–20. [Online]. Available: http://dl.acm.org/citation.cfm?id=1855511.1855531.

  76. D. Tsirogiannis, S. Harizopoulos, and M. A. Shah, “Analyzing the energy efficiency of a database server,” in Proceedings of the 2010 international conference on Management of data, ser. SIGMOD'10. New York, NY, USA: ACM, 2010, pp. 231–242. [Online]. Available: http://doi.acm.org/10.1145/1807167.1807194.

  77. T. Härder, V. Hudlet, Y. Ou, and D. Schall, “Energy efficiency is not enough, energy proportionality is needed!” in Proceedings of the 16th international conference on Database systems for advanced applications, ser. DASFAA'11. Berlin, Heidelberg: Springer-Verlag, 2011, pp. 226–239. [Online]. Available: http://dl.acm.org/citation.cfm?id=1996686.1996716.

  78. Y. Deng, “What is the future of disk drives, death or rebirth?” ACM Comput. Surv., vol. 43, no. 3, pp. 23:1–23:27, Apr. 2011. [Online]. Available: http://doi.acm.org.ezproxy1.lib.asu.edu/10.1145/1922649.1922660.

  79. H. Amur, J. Cipar, V. Gupta, G. R. Ganger, M. A. Kozuch, and K. Schwan, “Robust and flexible power-proportional storage,” in Proceedings of the 1st ACM symposium on Cloud computing, ser. SoCC'10. New York, NY, USA: ACM, 2010, pp. 217–228. [Online]. Available: http://doi.acm.org.ezproxy1.lib.asu.edu/10.1145/1807128.1807164.

  80. J. Guerra, W. Belluomini, J. Glider, K. Gupta, and H. Pucha, “Energy proportionality for storage: impact and feasibility,” SIGOPS Oper. Syst. Rev., vol. 44, pp. 35–39, March 2010. [Online]. Available: URL

    Google Scholar 

  81. C. H. Hsu and S. W. Poole, “Power signature analysis of the specpower_ssj2008 benchmark,” in IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2011. IEEE, 2011, pp. 227–236.

    Google Scholar 

  82. S. Wang, J. Liu, J.-J. Chen, and X. Liu, “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, vol. 1, no. 3, pp. 289–298, Sept. 2011.

    Google Scholar 

  83. E. Elnozahy, M. Kistler, and R. Rajamony, “Energy-efficient server clusters,” in Power-Aware Computer Systems, ser. Lecture Notes in Computer Science, B. Falsafi and T. Vijaykumar, Eds. Springer Berlin / Heidelberg, 2003, vol. 2325, pp. 179–197.

    Google Scholar 

  84. D. Meisner, C. M. Sadler, L. A. Barroso, W.-D. Weber, and T. F. Wenisch, “Power management of online data-intensive services,” in Proceeding of the 38th annual international symposium on Computer architecture, ser. ISCA'11. New York, NY, USA: ACM, 2011, pp. 319–330. [Online]. Available: http://doi.acm.org/10.1145/2000064.2000103.

  85. A. Gandhi, M. Harchol-Balter, and M. A. Kozuch, “The case for sleep states in servers,” in Proceedings of the 4th Workshop on Power-Aware Computing and Systems, ser. HotPower'11. New York, NY, USA: ACM, 2011, pp. 2:1–2:5. [Online]. Available: http://doi.acm.org/10.1145/2039252.2039254.

  86. Y. Wang, X. Wang, M. Chen, and X. Zhu, “Power-efficient response time guarantees for virtualized enterprise servers,” in Real-Time Systems Symposium, 2008, 30 2008-Dec. 3 2008, pp. 303–312.

    Google Scholar 

  87. X. Wang and Y. Wang, “Coordinating Power Control and Performance Management for Virtualized Server Clusters,” IEEE Transactions on Parallel and Distributed Systems, pp. 245–259, 2010.

    Google Scholar 

  88. P. Ranganathan, P. Leech, D. Irwin, and J. Chase, “Ensemble-level power management for dense blade servers,” in Computer Architecture, 2006. ISCA'06. 33rd International Symposium on, 0-0 2006, pp. 66–77.

    Google Scholar 

  89. T. Mukherjee, G. Varsamopoulos, S. SandeepK. Gupta, and S. Rungta, “Measurement-based power profiling of data center equipment,” in IEEE International Conference on Cluster Computing., Austin, Texas, USA, Sept. 2007, pp. 476–477.

    Google Scholar 

  90. B. Urgaonkar, P. Shenoy, A. Chandra, P. Goyal, and T. Wood, “Agile dynamic provisioning of multi-tier internet applications,” ACM Trans. Auton. Adapt. Syst., vol. 3, pp. 1:1–1:39, March 2008. [Online]. Available: http://doi.acm.org/10.1145/1342171.1342172.

  91. A. O. Allen, Probability, statistics and queuing theory with computer science applications. Academic Press Inc., 1990.

    Google Scholar 

  92. S. Saroiu, K. P. Gummadi, R. J. Dunn, S. D. Gribble, and H. M. Levy, “An analysis of Internet content delivery systems,” ACM SIGOPS Operating Systems Review, pp. 315–327, 2002.

    Google Scholar 

  93. M. Lin, Z. Liu, A. Wierman, and L. L. H. Andrew, “Online algorithms for geographical load balancing,” in Proc. of International Green Computing Conference (IGCC11). IEEE, June 2012.

    Google Scholar 

  94. P. Bohrer, E. N. Elnozahy, T. Keller, M. Kistler, C. Lefurgy, C. McDowell, and R. Rajamony, “The case for power management in web servers,” pp. 261–289, 2002.

    Google Scholar 

  95. P. Barford and M. Crovella, “Generating representative web workloads for network and server performance evaluation,” in Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems, ser. SIGMETRICS'98/PERFORMANCE'98. New York, NY, USA: ACM, 1998, pp. 151–160. [Online]. Available: http://doi.acm.org/10.1145/277851.277897.

  96. Y. Chen, A. Das, W. Qin, A. Sivasubramaniam, Q. Wang, and N. Gautam, “Managing server energy and operational costs in hosting centers,” SIGMETRICS Performance Evaluation Review, vol. 33, no. 1, pp. 303–314, 2005.

    Google Scholar 

  97. P. Bodik, R. Griffith, C. Sutton, A. Fox, M. I. Jordan, and D. A. Patterson, “Automatic exploration of datacenter performance regimes,” in Proceedings of the 1st workshop on Automated control for datacenters and clouds, ser. ACDC'09. New York, NY, USA: ACM, 2009, pp. 1–6. [Online]. Available: http://doi.acm.org/10.1145/1555271.1555273.

  98. I. Cunha, I. Viana, J. Palotti, J. Almeida, and V. Almeida, “Analyzing security and energy tradeoffs in autonomic capacity management,” in Network Operations and Management Symposium, 2008. NOMS 2008. IEEE. IEEE, 2008, pp. 302–309.

    Google Scholar 

  99. Q. Tang, S. K. S. Gupta, and G. Varsamopoulos, “Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: A cyber-physical approach,” IEEE Trans. Parallel Distrib. Syst., vol. 19, no. 11, pp. 1458–1472, 2008.

    Google Scholar 

  100. J. Moore, J. Chase, P. Ranganathan, and R. Sharma, “Making scheduling “cool”: temperature-aware workload placement in data centers,” in ATEC'05: Proceedings of the annual conference on USENIX Annual Technical Conference. Berkeley, CA, USA: USENIX Association, 2005, pp. 5–5.

    Google Scholar 

  101. J. Moore, J. Chase, and P. Ranganathan, “Weatherman: Automated, online, and predictive thermal mapping and management for data centers,” in IEEE International Conference on Autonomic Computing (ICAC), June 2006, pp. 155–164.

    Google Scholar 

  102. L. Parolini, N. Toliaz, B. Sinopoliy, and B. H. Kroghy, “A cyber-physical systems approach to energy management in data centers,” in ACM ICCPS'10, Stockholm, Sweden, April 2010.

    Google Scholar 

  103. Z. Abbasi, G. Varsamopoulos, and S. K. S. Gupta, “TACOMA: Server and workload management in internet data centers considering cooling-computing power trade-off and energy proportionality,” ACM Trans. Archit. Code Optim., vol. 9, no. 2, pp. 11:1–11:37, June 2012.

    Google Scholar 

  104. P. Sanders, N. Sivadasan, and M. Skutella, “Online scheduling with bounded migration,” Math. Oper. Res., vol. 34, no. 2, pp. 481–498, May 2009. [Online]. Available: URL

    Google Scholar 

  105. P. Padala, K.-Y. Hou, K. G. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal, and A. Merchant, “Automated control of multiple virtualized resources,” in Proceedings of the 4th ACM European conference on Computer systems. ACM, 2009, pp. 13–26.

    Google Scholar 

  106. R. Nathuji and K. Schwan, “Virtualpower: coordinated power management in virtualized enterprise systems,” SIGOPS Oper. Syst. Rev., vol. 41, pp. 265–278, Oct. 2007. [Online]. Available: http://doi.acm.org/10.1145/1323293.1294287.

  107. T. Gerald, K. J. Nicholas, D. Rajarshi, and N. B. Mohamed, “A hybrid reinforcement learning approach to autonomic resource allocation,” in IEEE International Conference on Autonomic Computing. IEEE, 2006, pp. 65–73.

    Google Scholar 

  108. J. Mars, L. Tang, R. Hundt, K. Skadron, and M. Soffa, “Bubble-up: Increasing utilization in modern warehouse scale computers via sensible co-locations,” in Proceedings of the 44th Annual IEEE/ACM International Symposium on Microarchitecture, ser. MICRO-44. New York, NY, USA: ACM, 2011, pp. 248–259. [Online]. Available: http://doi.acm.org/10.1145/2155620.2155650.

  109. J. Mars, L. Tang, and M. L. Soffa, “Directly characterizing cross core interference through contention synthesis,” in Proceedings of the 6th International Conference on High Performance and Embedded Architectures and Compilers, ser. HiPEAC'11. New York, NY, USA: ACM, 2011, pp. 167–176. [Online]. Available: http://doi.acm.org/10.1145/1944862.1944887.

  110. A. Fedorova, S. Blagodurov, and S. Zhuravlev, “Managing contention for shared resources on multicore processors,” Commun. ACM, vol. 53, no. 2, pp. 49–57, Feb 2010. [Online]. Available: http://doi.acm.org/10.1145/1646353.1646371.

  111. L. Tang, J. Mars, and M. L. Soffa, “Contentiousness vs. sensitivity: improving contention aware runtime systems on multicore architectures,” in Proceedings of the 1st International Workshop on Adaptive Self-Tuning Computing Systems for the Exaflop Era, ser. EXADAPT'11. New York, NY, USA: ACM, 2011, pp. 12–21. [Online]. Available: http://doi.acm.org/10.1145/2000417.2000419.

  112. R. C. Chiang and H. H. Huang, “Tracon: interference-aware scheduling for data-intensive applications in virtualized environments,” in Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, ser. SC'11. New York, NY, USA: ACM, 2011, pp. 47:1–47:12. [Online]. Available: http://doi.acm.org/10.1145/2063384.2063447.

  113. B. Kreaseck, L. Carter, H. Casanova, and J. Ferrante, “On the interference of communication on computation in Java,” International Parallel and Distributed Processing Symposium, vol. 15, p. 24–6, 2004.

    Google Scholar 

  114. Q. Zhu, J. Zhu, and G. Agrawal, “Power-aware consolidation of scientific workflows in virtualized environments,” in Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, ser. SC'10. Washington, DC, USA: IEEE Computer Society, 2010, pp. 1–12. [Online]. Available: http://dx.doi.org/10.1109/SC.2010.43.

  115. X. Pu, L. Liu, Y. Mei, S. Sivathanu, Y. Koh, C. Pu, Y. Cao, and L. Liu, “Who is your neighbor: Net i/o performance interference in virtualized clouds,” vol. PP, no. 99, 2012, pp. 1–1.

    Google Scholar 

  116. I. Paul., S. Yalamanchili., and L. K. J. John, “Performance impact of virtual machine placement in a datacenter,” in Performance Computing and Communications Conference (IPCCC), 2012 IEEE 31st International, 2012, pp. 424–431.

    Google Scholar 

  117. M. Kambadur, T. Moseley, R. Hank, and M. A. Kim, “Measuring interference between live datacenter applications,” in Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, ser. SC'12. Los Alamitos, CA, USA: IEEE Computer Society Press, 2012, pp. 51:1–51:12. [Online]. Available: http://dl.acm.org/citation.cfm?id=2388996.2389066.

  118. F. Hermenier, X. Lorca, J.-M. Menaud, G. Muller, and J. Lawall, “Entropy: a consolidation manager for clusters,” in Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments, ser. VEE'09. New York, NY, USA: ACM, 2009, pp. 41–50. [Online]. Available: http://doi.acm.org/10.1145/1508293.1508300.

  119. M. Pore, Z. Abbasi, S. Gupta, and G. Varsamopoulos, “Energy aware colocation of workload in data centers,” in 19th International Conference on High Performance Computing (HiPC), 2012, 2012, pp. 1–6.

    Google Scholar 

  120. A. Verma, P. Ahuja, and A. Neogi, “pmapper: power and migration cost aware application placement in virtualized systems,” in Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, ser. Middleware'08. New York, NY, USA: Springer-Verlag New York, Inc., 2008, pp. 243–264. [Online]. Available: http://dl.acm.org/citation.cfm?id=1496950.1496966.

  121. R. Buyya, A. Beloglazov, and J. H. Abawajy, “Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges,” CoRR, vol. abs/1006. 0308, 2010.

    Google Scholar 

  122. A. Merkel, J. Stoess, and F. Bellosa, “Resource-conscious scheduling for energy efficiency on multicore processors,” in Proceedings of the 5th European conference on Computer systems, ser. EuroSys'10. New York, NY, USA: ACM, 2010, pp. 153–166. [Online]. Available: http://doi.acm.org/10.1145/1755913.1755930.

  123. J. Mars, L. Tang, and R. Hundt, “Heterogeneity in homogeneous warehouse-scale computers: A performance opportunity,” Computer Architecture Letters, vol. 10, no. 2, pp. 29–32, July–Dec. 2011.

    Google Scholar 

  124. M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz, A. Konwinski, G. Lee, D. A. Patterson, A. Rabkin, and M. Zaharia, “Above the clouds: A berkeley view of cloud computing,” in Technical Report No. UCB/EECS-2009-28, University of California at Berkley, USA, February 2009.

    Google Scholar 

  125. K. Le, O. Bilgir, R. Bianchini, M. Martonosi, and T. D. Nguyen, “Managing the cost, energy consumption, and carbon footprint of internet services,” in Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems, ser. SIGMETRICS'10. New York, NY, USA: ACM, 2010, pp. 357–358.

    Google Scholar 

  126. “Quick start guide to increase data center energy efficiency,” U.S. Department of Energy, Tech. Rep., September 2010.

    Google Scholar 

  127. “Google data center more efficient that the industry average,” http://gigaom.com/2008/10/01/google-data-centers-more-efficient-than-the-industry-average/.

  128. J. Caruso, “Follow the moon, and save millions researchers highlight possibilities of data center energy savings.” March 2014. [Online]. Available: http://www.networkworld.com/newsletters/lans/2009/081709lan2.html.

  129. 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 Proceedings IEEE, March 2010, pp. 1–9.

    Google Scholar 

  130. S. Akoush, R. Sohan, A. Rice, A. W. Moore, and A. Hopper, “Free lunch: exploiting renewable energy for computing,” in Proceedings of HotOS, 2011.

    Google Scholar 

  131. M. Etinski, M. Martonosi, K. Le, R. Bianchini, and T. D. Nguyen, “Optimizing the use of request distribution and stored energy for cost reduction in multi-site internet services,” in Sustainable Internet and ICT for Sustainability (SustainIT), 2012. IEEE, 2012, pp. 1–10.

    Google Scholar 

  132. Z. Abbasi, T. Mukherjee, G. Varsamopoulos, and S. K. S. Gupta, “Dahm: A green and dynamic web application hosting manager across geographically distributed data centers,” J. Emerg. Technol. Comput. Syst., vol. 8, no. 4, pp. 34:1–34:22, Nov. 2012.

    Google Scholar 

  133. Y. Zhang, Y. Wang, and X. Wang, “Greenware: greening cloud-scale data centers to maximize the use of renewable energy,” Middleware 2011, pp. 143–164, 2011.

    Google Scholar 

  134. Z. Liu, M. Lin, A. Wierman, S. H. Low, and L. L. H. Andrew, “Geographical load balancing with renewables,” ACM SIGMETRICS Performance Evaluation Review, vol. 39, no. 3, pp. 62–66, 2011.

    Google Scholar 

  135. C. Stewart and K. Shen, “Some joules are more precious than others: Managing renewable energy in the datacenter,” in Proceedings of the Workshop on Power Aware Computing and Systems, 2009.

    Google Scholar 

  136. Z. Abbasi, M. Pore, and S. K. Gupta, “Online server and workload management for joint optimization of electricity cost and carbon footprint across data centers,” in 28th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2014.

    Google Scholar 

  137. D. S. Palasamudram, R. K. Sitaramanz, B. Urgaonkar, and R. Urgaonkar, “Using batteries to reduce the power costs of internet-scale distributed networks,” in Proceedings of 2012 ACM Symposium on Cloud Computing. ACM, Oct. 2012, Palasamudram 2012 using.

    Google Scholar 

  138. S. Govindan, D. Wang, Anand, Sivasubramaniam, and B. Urgaonkar, “Leveraging stored energy for handling power emergencies in aggressively provisioned datacenters,” in Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). ACM, 2012, pp. 75–86.

    Google Scholar 

  139. V. Kontorinis, L. E. Zhang, B. Aksanli, J. Sampson, H. Homayoun, E. Pettis, D. M. Tullsen, and T. S. Rosing, “Managing distributed ups energy for effective power capping in data centers,” in Proceedings of the 39th International Symposium on Computer Architecture, ser. ISCA'12. Piscataway, NJ, USA: IEEE Press, 2012, pp. 488–499. [Online]. Available: http://dl.acm.org/citation.cfm?id=2337159.2337216.

  140. D. Wang, C. Ren, A. Sivasubramaniam, B. Urgaonkar, and H. Fathy, “Energy storage in datacenters: what, where, and how much?” in Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems, ser. SIGMETRICS'12. New York, NY, USA: ACM, 2012, pp. 187–198, wang2012-energy-storage.

    Google Scholar 

  141. K. Le, R. Bianchini, T. D. Nguyen, O. Bilgir, and M. Martonosi, “Capping the brown energy consumption of internet services at low cost,” in Green Computing Conference, 2010 International. IEEE, 2010, pp. 3–14.

    Google Scholar 

  142. P. X. Gao, A. R. Curtis, B. Wong, and S. Keshav, “It’s not easy being green,” ACM SIGCOMM Computer Communication Review, vol. 42, no. 4, pp. 211–222, 2012.

    Google Scholar 

  143. S. Ren and Y. He, “Coca: Online distributed resource management for cost minimization and carbon neutrality in data centers,” in Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, ser. SC'13. New York, NY, USA: ACM, 2013, pp. 39:1–39:12. [Online]. Available: http://doi.acm.org/10.1145/2503210.2503248

  144. A. H. Mahmud and S. Ren, “Online capacity provisioning for carbon-neutral data center with demand-responsive electricity prices,” ACM SIGMETRICS Performance Evaluation Review, vol. 41, no. 2, pp. 26–37, 2013.

    Google Scholar 

  145. Z. Zhou, F. Liu, Y. Xu, R. Zou, H. Xu, J. C. S. Lui, and H. Jin, “Carbon-aware load balancing for geo-distributed cloud services,” in Proceedings of the 2013 IEEE 21st International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems, ser. MASCOTS'13. Washington, DC, USA: IEEE Computer Society, 2013, pp. 232–241. [Online]. Available: http://dx.doi.org/10.1109/MASCOTS.2013.31.

  146. J. Doyle, R. Shorten, and D. O’Mahony, “Stratus: Load balancing the cloud for carbon emissions control,” Cloud Computing, IEEE Transactions on, vol. 1, no. 1, pp. 1–1, Jan 2013.

    Google Scholar 

  147. D. Xu and X. Liu, “Geographic trough filling for internet datacenters,” in IEEE Proceedings INFOCOM. IEEE, 2012, pp. 2881–2885.

    Google Scholar 

  148. S.-H. Lim, B. Sharma, G. Nam, E. K. Kim, and C. Das, “Mdcsim: A multi-tier data center simulation, platform,” in Cluster Computing and Workshops, 2009. CLUSTER'09. IEEE International Conference on, Aug 2009, pp. 1–9.

    Google Scholar 

  149. D. Meisner, J. Wu, and T. Wenisch, “Bighouse: A simulation infrastructure for data center systems,” in Performance Analysis of Systems and Software (ISPASS), 2012 IEEE International Symposium on, April 2012, pp. 35–45.

    Google Scholar 

  150. R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. De Rose, and R. Buyya, “Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software: Practice and Experience, vol. 41, no. 1, pp. 23–50, 2011.

    Google Scholar 

  151. C. Patel, C. E. Bash, C. Belady, L. Stahl, and D. Sullivan, “Computational fluid dynamics modeling of high compute density data centers to assure system inlet air specifications,” in ASME International Electronic Packaging Technical Conference and Exhibition (IPACK'01), 2001, patel_ipack2001.

    Google Scholar 

  152. L. Marshall and P. Bems, “Using cfd for data center design and analysis,” Applied Math Modeling, Tech. Rep., Jan. 2011, White Paper.

    Google Scholar 

  153. U. Singh, “Cfd-based operational thermal efficiency improvement of a production data center,” in Proceedings of the First USENIX conference on Sustainable information technology, ser. SustainIT'10. Berkeley, CA, USA: USENIX Association, 2010, pp. 6–6, sI2010USENIX.

    Google Scholar 

  154. S. K. S. Gupta, R. R. Gilbert, A. Banerjee, Z. Abbasi, T. Mukherjee, and G. Varsamopoulos, “GDCSim - an integrated tool chain for analyzing green data center physical design and resource management techniques,” in International Green Computing Conference (IGCC), Orlando, FL, 2011, pp. 1–8.

    Google Scholar 

  155. S. K. S. Gupta, A. Banerjee, Z. Abbasi, G. Varsamopoulos, M. Jonas, J. Ferguson, R. R. Gilbert, and T. Mukherjee, “Gdcsim: A simulator for green data center design and analysis,” ACM Trans. Model. Comput. Simul., vol. 24, no. 1, pp. 3:1–3:27, Jan 2014. [Online]. Available: http://doi.acm.org/10.1145/2553083

  156. A. Banerjee, J. Banerjee, G. Varsamopoulos, Z. Abbasi, and S. K. Gupta, “Hybrid simulator for cyber-physical energy systems,” in 2013 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES). IEEE, 2013, pp. 1–6.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Computing and Informatics, Ira A. Fulton School of Engineering, Arizona State University, Tempe, AZ, 85281, USA

    Madhurima Pore, Zahra Abbasi, Sandeep K. S. Gupta & Georgios Varsamopoulos

Authors
  1. Madhurima Pore
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zahra Abbasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sandeep K. S. Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Georgios Varsamopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Madhurima Pore .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, North Dakota State University, Fargo, North Dakota, USA

    Samee U. Khan

  2. School of Information Technologies, The University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this chapter

Cite this chapter

Pore, M., Abbasi, Z., Gupta, S., Varsamopoulos, G. (2015). Techniques to Achieve Energy Proportionality in Data Centers: A Survey. In: Khan, S., Zomaya, A. (eds) Handbook on Data Centers. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2092-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-2092-1_4

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-2091-4

  • Online ISBN: 978-1-4939-2092-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation