Skip to main content

Overview

  • Chapter
  • First Online:
  • 361 Accesses

Abstract

This chapter gives a high-level overview of multi-tenant data centers which are an important part of the data center industry. Their efficiency has fallen behind that of owner-operated data centers due to the use of an uncoordinated model. Coordinated power management is proposed as a solution to improve the efficiency of multi-tenant data centers. To achieve coordinated power management, the operators must address the dynamic natures of both the supply and demand sides of the multi-tenant data center (in terms of computation which is a function of energy consumption). The supply side comprises the grid, on-site renewables, storage (batteries), and backup generator, whereas the demand side comprises the sum of individual demands from tenants.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    For instance, the world’s largest (retail) multi-tenant data center for physical resources is Equinix [21].

  2. 2.

    Cloud providers themselves can be tenants of multi-tenant data centers with physical resources, e.g., Amazon Web Services house some servers in multi-tenant data centers around the world to reduce latency [22].

  3. 3.

    Hereafter, we use multi-tenant data center operator and operator interchangeably.

References

  1. Ren, S. & Islam, M. A. (2014, June). Colocation demand response: Why do I turn off my servers? In 11th international conference on autonomic computing (pp. 201–208). Philadelphia, PA: USENIX Association. [Online]. Available: https://goo.gl/DT5tmP.

  2. Data Center Colocation Market by Service Types (Retail, Wholesale), Industry Verticals (Banking & Insurance, IT & Telecom, Healthcare, Government & Public, Energy), End Users (SMEs, Large Enterprises) & by Regions – Global Forecast to 2020. (2016, January). Research and markets, Tech. Rep. [Online]. Available: http://goo.gl/GA3mwG.

  3. Cisco. (2016, April). Cisco Global Cloud Index: Forecast and Methodology, 2014–2019 White Paper. Tech. Rep. [Online]. Available: http://goo.gl/DOfl54.

  4. Shehabi, A., Smith, S. J., Sartor, D. A., Brown, R. E., Herrlin, M., Koomey, J. G., et al. (2016, June). United States Data Center Energy Usage Report. [Online]. Available: http://goo.gl/Y93L9t.

  5. Chao, J. (2016, June). Data centers continue to proliferate while their energy use plateaus. Berkeley Lab News Center Website. [Online]. Available: https://goo.gl/5T3Hxf.

  6. Electricity in the U.S. Webpage. [Online]. Available: http://goo.gl/xR3p6L.

  7. Wikipedia, Electricity Generation. Wikipedia Website. [Online]. Available: https://goo.gl/EL4e8o.

  8. U.S. Federal Energy Regulatory Commission. (2010, June). National action plan on demand response. Tech. Rep. [Online]. Available: http://goo.gl/v0MPVe.

  9. U.S. Federal Energy Regulatory Commission. (2012, December). Assessment of demand response and advanced metering. Tech. Rep. [Online]. Available: http://goo.gl/cvg8qy.

  10. dePreaux, J. (2013). Wholesale and Retail Data Centers – North America and Europe – 2013, IHS, Tech. Rep. [Online]. Available: https://goo.gl/soiFoh.

  11. Whitney, J. & Delforge, P. (2014, August). Scaling up energy efficiency across the data center industry: Evaluating key drivers and barriers. NRDC and Anthesis, Tech. Rep. [Online]. Available: http://goo.gl/cYHNIW.

  12. Stansberry, M. (2016). 2016 Data Center Industry Survey, Uptime Institute, Tech. Rep. [Online]. Available: https://goo.gl/j6gero.

  13. CoreSite. (2012). Improving IT efficiencies: Four advantages of multi-tenant data centers. Tech. Rep. white Paper. [Online]. Available: https://goo.gl/UMgamU.

  14. Equinix Website. [Online]. Available: http://www.equinix.com/.

  15. Amazon.com. Amazon Web Services: Global Infrastructure. Amazon.com Website. [Online]. Available: https://goo.gl/YuRsRs.

  16. Cook, G., Dowdall, T., Pomerantz, D., & Wang, Y. (2014, April). Clicking clean: How companies are creating the green internet. Greenpeace, Tech. Rep. [Online]. Available: http://goo.gl/IF58Bz.

  17. Microsoft. Microsoft: Global infrastructure. Microsoft Website. [Online]. Available: https://goo.gl/MLGIcP.

  18. Kerrigan, J. (2016, January). January 2016 Report: 2015 Year in Review. North American Data Centers, Tech. Rep. [Online]. Available: https://goo.gl/SFgTzy.

  19. Kerrigan, J. (2016, April). NewsleNews – Spotlight on NorNorth Virginia. North American Data Centers, Tech. Rep. [Online]. Available: https://goo.gl/s3CKWT.

  20. CyrusOne. (2012). Colocation: The logical home for the cloud. Computer World, Tech. Rep. [Online]. Available: http://goo.gl/vsa26h.

  21. Miller, R. (2014, February). Inside SuperNAP 8: Switch’s Tier IV Data Fortress. Data Center Knowledge Website. [Online]. Available: http://goo.gl/nVEecc.

  22. Shen, Y. (2014, September). The shift to content delivery networks (CDNs) supports more and better customer video experiences. Website, cisco. [Online]. Available: http://goo.gl/s7SgXh.

  23. Barroso, L. A., Clidaras, J., & Hoelzle, U. (2013). In M. D. Hill (Ed.), The datacenter as a computer: An introduction to the design of warehouse-scale machines (2nd ed.). San Rafael, CA: Morgan and Claypool Publishers. [Online]. Available: http://goo.gl/GPHC3U.

  24. Telegeography Website. [Online]. Available: https://www.telegeography.com/.

  25. Kong, F. & Liu, X. (2014, November). A survey on green-energy-aware power management for datacenters. ACM Computing Surveys, 47(2), 30:1–30:38. [Online]. Available: http://goo.gl/8JaPk1.

  26. Hammadi, A. & Mhamdi, L. (2014). A survey on architectures and energy efficiency in data center networks. Computer Communications, 40, 1–21. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0140366413002727.

  27. Amur, H., Cipar, J., Gupta, V., Ganger, G. R., Kozuch, M. A., & Schwan, K. (2010). Robust and flexible power-proportional storage. In 1st ACM Symposium on Cloud Computing, ser. SoCC ’10 (pp. 217–228). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/OHbPql.

  28. Lin, M., Wierman, A., Andrew, L., & Thereska, E. (2011, April). Dynamic right-sizing for power-proportional data centers. In IEEE INFOCOM (pp. 1098–1106). [Online]. Available: http://goo.gl/QoqujE.

  29. Xu, Z., Wang, X., & cheng Tu, Y. (2013). Power-aware throughput control for database management systems. In 10th international conference on autonomic computing, (ICAC 13) (pp. 315–324). San Jose, CA: USENIX. [Online]. Available: https://goo.gl/nuaiXn.

  30. Qureshi, A., Weber, R., Balakrishnan, H., Guttag, J., & Maggs, B. (2009). Cutting the electric bill for internet-scale systems. In ACM SIGCOMM Conference on Data Communication, ser. SIGCOMM ’09 (pp. 123–134). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/aV7dMs.

  31. Rao, L., Liu, X., Xie, L., & Liu, W. (2010, March). Minimizing electricity cost: Optimization of distributed internet data centers in a multi-electricity-market environment. In IEEE INFOCOM (pp. 1–9). [Online]. Available: http://goo.gl/ccIfXf.

  32. Xu, H. & Li, B. (2014). Reducing electricity demand charge for data centers with partial execution. In 5th international conference on future energy systems, ser. e-Energy ’14 (pp. 51–61). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/Li8eq5.

  33. Aksanli, B., Venkatesh, J., Zhang, L., & Rosing, T. (2011). Utilizing green energy prediction to schedule mixed batch and service jobs in data centers. In 4th workshop on power-aware computing and systems, ser. HotPower ’11 (pp. 5:1–5:5). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/jYY4PF.

  34. Deng, N., Stewart, C., Gmach, D., Arlitt, M., & Kelley, J. (2012). Adaptive green hosting. In 9th international conference on autonomic computing, ser. ICAC ’12 (pp. 135–144). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/Ha9wWT.

  35. Goiri, I., Le, K., Nguyen, T. D., Guitart, J., Torres, J., & Bianchini, R. (2012). GreenHadoop: Leveraging green energy in data-processing frameworks. In 7th ACM European conference on computer systems, ser. EuroSys ’12 (pp. 57–70). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/CE5FC2.

  36. Liu, Z., Lin, M., Wierman, A., Low, S. H., & Andrew, L. L. (2011). Greening geographical load balancing. In ACM SIGMETRICS joint international conference on measurement and modeling of computer systems, ser. SIGMETRICS ’11 (pp. 233–244). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/DHgS7v.

  37. Zhang, Y., Wang, Y., & Wang, X. (2011). GreenWare: Greening cloud-scale data centers to maximize the use of renewable energy. In 12th ACM/IFIP/USENIX international conference on middleware, ser. Middleware’11 (pp. 143–164). Berlin/Heidelberg: Springer. [Online]. Available: http://goo.gl/3zAgMu.

  38. Chen, J., Tan, R., Wang, Y., Xing, G., Wang, X., Wang, X., et al. (2012, December). A high-fidelity temperature distribution forecasting system for data centers. In IEEE 33rd real-time systems symposium, (RTSS) (pp. 215–224). [Online]. Available: http://goo.gl/tssu9a.

  39. Tang, Q., Gupta, S., & Varsamopoulos, G. (2007, September). Thermal-aware task scheduling for data centers through minimizing heat recirculation. In IEEE international conference on cluster computing (pp. 129–138). [Online]. Available: http://goo.gl/maseGi.

  40. Govindan, S., Wang, D., Sivasubramaniam, A., & Urgaonkar, B. (2012). Leveraging stored energy for handling power emergencies in aggressively provisioned datacenters. In 17th international conference on architectural support for programming languages and operating systems, ser. ASPLOS XVII (pp. 75–86). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/6kTnGL.

  41. Palasamudram, D. S., Sitaraman, R. K., Urgaonkar, B., & Urgaonkar, R. (2012). Using batteries to reduce the power costs of internet-scale distributed networks. In 3rd ACM symposium on cloud computing, ser. SoCC ’12 (pp. 11:1–11:14). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/fGH9ts.

  42. Urgaonkar, R., Urgaonkar, B., Neely, M. J., & Sivasubramaniam, A. (2011). Optimal power cost management using stored energy in data centers. In ACM SIGMETRICS joint international conference on measurement and modeling of computer systems, ser. SIGMETRICS ’11 (pp. 221–232). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/HKg8vj.

  43. Gandhi, A., Harchol-Balter, M., Das, R., & Lefurgy, C. (2009). Optimal power allocation in server farms. In 11th international joint conference on measurement and modeling of computer systems, ser. SIGMETRICS ’09 (pp. 157–168). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/HOOx9P.

  44. Wang, D., Ren, C., & Sivasubramaniam, A. (2013). Virtualizing power distribution in datacenters. In 40th annual international symposium on computer architecture, ser. ISCA ’13 (pp. 595–606). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/p9AViH.

  45. Liu, H., Xu, C.-Z., Jin, H., Gong, J., & Liao, X. (2011). Performance and energy modeling for live migration of virtual machines. In 20th international symposium on high performance distributed computing, ser. HPDC ’11 (pp. 171–182). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/t7bh5K.

  46. Urgaonkar, R., Kozat, U., Igarashi, K., & Neely, M. (2010, April). Dynamic resource allocation and power management in virtualized data centers. In IEEE network operations and management symposium, (NOMS) (pp. 479–486). [Online]. Available: http://goo.gl/mBdOhd.

  47. Yang, C.-T., Wang, K.-C., Cheng, H.-Y., Kuo, C.-T., & Chu, W. C. C. (2011). Green power management with dynamic resource allocation for cloud virtual machines. In IEEE international conference on high performance computing and communications, ser. HPCC ’11 (pp. 726–733). Washington, DC, USA: IEEE Computer Society. [Online]. Available: http://goo.gl/WUSfsB.

  48. Aikema, D., Simmonds, R., & Zareipour, H. (2012, June). Data centers in the ancillary services market. In International green computing conference, (IGCC) (pp. 1–10). [Online]. Available: https://goo.gl/JwZ8q6.

  49. Ghatikar, G., Ganti, V., Matson, N., & Piette, M. A. (2012, August). Demand response opportunities and enabling technologies for data centers: Findings from field studies. Berkeley Lab, Berkeley, CA, Tech. Rep. LBNL-5763E. [Online]. Available: http://goo.gl/Z953FG.

  50. Li, S., Brocanelli, M., Zhang, W., & Wang, X. (2013, July). Data center power control for frequency regulation. In IEEE power and energy society general meeting, (PES) (pp. 1–5). [Online]. Available: http://goo.gl/E7A9IO.

  51. Liu, Z., Liu, I., Low, S., & Wierman, A. (2014). Pricing data center demand response. In ACM international conference on measurement and modeling of computer systems, ser. SIGMETRICS ’14 (pp. 111–123). New York, NY, USA: ACM. [Online]. Available: https://goo.gl/4U2Ihs.

  52. Wang, H., Huang, J., Lin, X., & Mohsenian-Rad, H. (2014, January). Exploring smart grid and data center interactions for electric power load balancing. SIGMETRICS Performance Evaluation Review, 41(3), 89–94. [Online]. Available: http://goo.gl/Fn1Dny.

  53. Apple. (2016). Environmental Responsibility Report 2016, Tech. Rep. [Online]. Available: http://goo.gl/iffeRQ.

  54. Jones, P. (2014, April). Greenpeace gives digital realty, equinix low grades on renewable energy. Data Center Dynamics Website. [Online]. Available: http://goo.gl/lQofPp.

  55. Novet, J. (2013, November). Colocation providers, customers trade tips on energy savings. Data Center Knowledge Website. [Online]. Available: http://goo.gl/BygHRY.

  56. Verizon. Pricing plan. Verizon Website. [Online]. Available: http://goo.gl/NsyAbu.

  57. Miller, R. (2011, February). Analysis: Colocation pricing trends. Data Center Knowledge Website. [Online]. Available: http://goo.gl/NvOjLl.

  58. PG&E. Pacific gas and electric company. PG&E Website. [Online]. Available: http://www.pge.com/.

  59. Liu, Z., Wierman, A., Chen, Y., Razon, B., & Chen, N. (2013, June). Data center demand response: Avoiding the coincident peak via workload shifting and local generation. SIGMETRICS Performance Evaluation Review, 41(1), 341–342. [Online]. Available: http://goo.gl/rvenlD.

  60. Wang, C., Urgaonkar, B., Wang, Q., & Kesidis, G. (2014, September). A hierarchical demand response framework for data center power cost optimization under real-world electricity pricing. In IEEE 22nd international symposium on modelling, analysis simulation of computer and telecommunication systems, (MASCOTS) (pp. 305–314). [Online]. Available: http://goo.gl/ootCBH.

  61. Yao, J., Liu, X., He, W., & Rahman, A. (2012, June). Dynamic control of electricity cost with power demand smoothing and peak shaving for distributed internet data centers. In IEEE 32nd international conference on distributed computing systems (pp. 416–424). [Online]. Available: http://goo.gl/15pNSh.

  62. Apple. Environmental responsibility. Apple Website. [Online]. Available: http://goo.gl/syQ0Pz.

  63. Li, C., Hu, Y., Zhou, R., Liu, M., Liu, L., Yuan, J., et al. (2013). Enabling datacenter servers to scale out economically and sustainably. In 46th annual IEEE/ACM international symposium on microarchitecture, ser. MICRO-46 (pp. 322–333). New York, NY, USA: ACM. [Online]. Available: http://goo.gl/x3xA1z.

  64. Le, K., Bianchini, R., Nguyen, T., Bilgir, O., & Martonosi, M. (2010, August). Capping the brown energy consumption of internet services at low cost. In International green computing conference (pp. 3–14). [Online]. Available: http://goo.gl/mvrGSQ.

  65. International Renewable Energy Agency. (2012, June). Renewable energy cost analysis – solar photovoltaics. Tech. Rep. [Online]. Available: http://goo.gl/NNNJCw.

  66. Muenzel, V., Mareels, I., de Hoog, J., Vishwanath, A., Kalyanaraman, S., & Gort, A. (2015, February). PV generation and demand mismatch: Evaluating the potential of Residential Storage. In Innovative smart grid technologies conference (ISGT), 2015 IEEE Power Energy Society (pp. 1–5). [Online]. Available: http://goo.gl/CymZ50.

  67. U.S. Government. (2015, March). U.S. Federal Leadership on Climate Change and Environmental Sustainability – EXECUTIVE ORDER 13693. Whitehouse Website. [Online]. Available: https://goo.gl/05Rj04.

  68. Akamai. Environmental sustainability policy. Akamai Website. [Online]. Available: https://goo.gl/Mdwb8D.

  69. Califonia energy efficiency potential and goals studies. Calfonia Government Website. [Online]. Available: http://goo.gl/7jkPXa.

  70. Greenberg, A., Hamilton, J., Maltz, D. A., & Patel, P. (2008, December). The cost of a cloud: Research problems in data center networks. SIGCOMM Computer Communication Review, 39(1), 68–73. [Online]. Available: http://goo.gl/IOPy6d.

  71. U.S. EPA. (2012, November). Utility guide for designing incentive programs focused on data center efficiency measures. Tech. Rep. [Online]. Available: https://goo.gl/E91etC.

  72. U.S. Green Building Council Std. Leadership in energy & environmental design. [Online]. Available: http://goo.gl/jqGiox.

  73. Whitehouse. (2009, October). U.S. Federal leadership in environmental, energy, and economic performance – EXECUTIVE ORDER 13514 Whitehouse Website.

    Google Scholar 

  74. wikipedia. Demand response. Wikipedia Website. [Online]. Available: https://goo.gl/RCEpe8.

  75. Brown, R. E., Masanet, E. R., Nordman, B., Tschudi, W. F., Shehabi, A., Stanley, J., et al. (2008, June). Report to congress on server and data center energy efficiency: Public law 109-431. Berkeley Lab, Berkeley, CA, Tech. Rep. LBNL-363E. [Online]. Available: https://goo.gl/Vi8IS7.

  76. EnerNOC. (2013). Ensuring U.S. grid security and reliability: U.S. EPAs proposed emergency backup generator rule. Tech. Rep. [Online]. Available: https://goo.gl/Pzj5pE.

  77. Wierman, A., Liu, Z., Liu, I., & Mohsenian-Rad, H. (2014, November). Opportunities and challenges for data center demand response. In IEEE international green computing conference, Dallas, TX. [Online]. Available: http://goo.gl/HDdXXH.

  78. Demand response. PJM Website. [Online]. Available: https://goo.gl/GtU2sR.

  79. PJM. (2015, December). Load Management Performance Report – 2014/2015. PJM, Tech. Rep. [Online]. Available: https://goo.gl/2CfzHo.

  80. Aksanli, B. & Rosing, T. (March 2014). Providing regulation services and managing data center peak power budgets. In Design, automation and test in Europe conference and exhibition, (DATE) (pp. 1–4). [Online]. Available: http://goo.gl/3UYoOk.

  81. Data Center Map: Colocation, U.S.A.. Data Center Map Website. [Online]. Available: http://goo.gl/JkV6tg.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Oo, T.Z., Tran, N.H., Ren, S., Hong, C.S. (2018). Overview. In: A Survey on Coordinated Power Management in Multi-Tenant Data Centers. Springer, Cham. https://doi.org/10.1007/978-3-319-66062-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-66062-2_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66061-5

  • Online ISBN: 978-3-319-66062-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics