Bringing Enterprise IT into the 21st Century: A Management and Sustainability Challenge


Published on

I gave this talk as a webinar on March 19th, 2014 for the Corporate Eco Forum. It discusses ways to improve the efficiency of enterprise IT, mainly focusing on institutional changes that are necessary to make modern IT organizations perform effectively. It draws upon our case study of eBay as well as my other work on data centers over the years.

Published in: Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Bringing Enterprise IT into the 21st Century: A Management and Sustainability Challenge

  1. 1. Bringing enterprise computing into the 21st century: A management and sustainability challenge Jonathan Koomey, Ph.D. Research Fellow, Steyer-Taylor Center for Energy Policy and Finance, Stanford University Webinar for the Corporate Eco Forum March 19, 2014 1
  2. 2. For more details on the topic of this presentation, see article at inging-enterprise-computing-21st- century-management-sustainability- challenge/ See presentation at 2
  3. 3. Key conclusion: In most companies, enterprise computing is designed using decades old assumptions and techniques, and but fixing it is more of a management problem than a technology problem 3
  4. 4. Introduction to information technology (IT) 4
  5. 5. The big picture view Source: Ericsson and TeliaSonera (Malmodin et al. 2013) with support from CESC, KTH Sweden 5
  6. 6. Delivery of IT services is increasing rapidly 6
  7. 7. Network data flows over time Source: 1986 to 2007 adapted from Hilbert et. al. 2011; 2014 extrapolated using Cisco VNI data compiled at Doubling time 1986 to 2014 = 3 years, doubling time 2000 to 2014 = 1.5 years. 1986 1993 2000 2007 2014E Mobile data Fixed Internet Voice 7
  8. 8. At the same time, information technology is becoming more energy efficient at a furious pace 8
  9. 9. Computing efficiency at full load doubles every 1.6 years Source: Koomey et al. 2011 9
  10. 10. But growth in data use doesn’t necessarily imply growth in electricity use 10
  11. 11. Two opposing factors • Equivalently • So if Computations/Year goes up faster than Computations/kWh, then total kWh goes up! 11
  12. 12. Kinds of data centers • Hyperscale (e.g., Google, Facebook, Microsoft, eBay, others) • Enterprise or “in-house” (vast majority) – Conventional – Internal cloud (similar to hyperscale) • Co-location (my facility, your IT) • High Performance Computing (special case– batch jobs, very high utilization) 12
  13. 13. Electricity Flows in Data Centers 13 Source: Koomey/Mares 13
  14. 14. Annualized costs mostly IT, but infrastructure costs not trivial Copyright Jonathan G. Koomey 2013 14 Adapted from data in Koomey et al. 2007
  15. 15. Data centers used 1.3% of global electricity and 2% of US electricity in 2010* *For details see Koomey 2011 15
  16. 16. Data center electricity use worldwide Source: Koomey 2011. Graph shows worldwide numbers. For the US, the range for data centers in 2010 was 1.7 to 2.2% of the total. N.B. Infrastructure in this slide refers to cooling, fans, pumps, and power distribution inside data centers. 16
  17. 17. Server installed base Source: IDC 2013 Vernon Turner 17
  18. 18. Many efficiency opportunities, particularly in IT equipment Source: Masanet et al. 2011 18
  19. 19. Improving the energy efficiency of data centers is as much about people and institutions as it is about technology 19Copyright Jonathan G. Koomey 2013
  20. 20. Why asset management is key Slide courtesy of Winston Saunders, Intel 20
  21. 21. Idle power improving: Server power curves (via Intel) • Usage Driven • Variable Utilization • Proportional Energy Use • Optimized Efficiency • Technology Scope: • CPU and Memory • Power Delivery, Fans, etc. • Instrumentation Approaching “Ideal” Server Behavior IN THEORY Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark* and MobileMark*, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. Configurations: Dual Socket Server. For full configuration information, please see backup. For more information go to Xeon™ 5160 Xeon™ E5-2660 2012 2006 Data from Source: Winston Saunders, Intel 21
  22. 22. Appropriate metrics are critical for driving organizational change • Need to focus on – reducing total costs per computation – increasing total value from computation • Need to be able to “show back” the consequences of choices to every employee • Need also to calculate Key Performance Indicators (KPIs) for management 22
  23. 23. DSE 23
  24. 24. After metrics, you also need to… • combine budgets and responsibilities • move toward cloud deployment for many IT functions – instant user access (not weeks or months) – easier forecasting of needs (commoditized computing) • move from “sit down” IT deployment to “buffet style” for those who still need customized IT – reduce # of server SKUs and configurations – have standard configurations “in stock” 24
  25. 25. Energy-related advantages of hyperscale/cloud computing • Low PUE • Diversity of users • Economies of scale • Flexibility (because of abstraction/virtualization) • Easier provisioning for outside users ∑: Costs and energy use per computation much lower than conventional enterprise/colo installations 25
  26. 26. Next, use mass production, predictive modeling, and integrated design • Standardized IT deployments – Modular systems, or – Scalable infrastructure with commoditized and homogeneous IT hardware • Measure, experiment, learn, and replicate • Use predictive models to understand air flows before you alter your existing facilities • Focus on whole-system integrated design to deliver computing services ever more effectively 26
  27. 27. Example: How to tackle data center greenhouse gas emissions? • DC GHG emissions affected by 3 factors – Infrastructure efficiency (PUE) – IT efficiency – Emissions intensity of electricity • Best to focus on value, costs, and emissions per computation, not narrow efficiency metrics • More computations means higher total business value, lower costs per computation, and higher profits 27
  28. 28. PUE isn’t everything 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Relative Data Center GHG Emissions (Baseline = 1) RelativeDataCenterEnergyUse(Baseline=1) [6] [5] [4] [3] [1] [2] PUE=1.8, minimal IT efficiency PUE=1.5, minimal IT efficiency PUE=1.3 (free cooling, warm climate), minimal IT efficiency PUE=1.1 (free cooling, cool climate), minimal IT efficiency Decreasing electric power CO2 intensity Increasingoperationalenergyefficiency High energy, high carbon region High energy, low carbon region Low energy, low carbon region Baseline data center powered by coal: - Energy use = 92 GWh/yr - GHG emissions = 89 kt CO2e/yr PUE=1.1, maximal IT efficiency PUE=1.8, maximal IT efficiency [D] Coal (0.96 kt CO2e/GWh) [C] U.S. average electricity (0.6 kt CO2e/GWh) [B] Natural gas SOFC (0.35 kt CO2e/GWh) [A] Renewables (~0.02 kt CO2e/GWh) Source: Masanet et al. 2013 28
  29. 29. Data center lessons • Biggest inefficiencies in enterprise data centers (cloud providers much better) • Just adopting best practices will save 80+% • IT efficiency most important, followed by infrastructure efficiency and sourcing of low- carbon electricity (embedded emissions not so important) • Biggest impediments to efficiency are institutional and cognitive, not technical 29
  30. 30. ∑: IT should NOT be treated as a cost center, it should be a cost reducing profit center that also improves corporate and customer environmental performance 30
  31. 31. Suggested reading 31 Brynjolfsson, Erik, and Andrew McAffee. 2014. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York, NY: W. W. Norton & Company. []
  32. 32. References • Brynjolfsson, Erik, and Lorin M. Hitt. 2000. "Beyond Computation: Information Technology, Organizational Transformation and Business Performance." Journal of Economic Perspectives. vol. 14, no. 4. Fall. pp. 23-48. • Hilbert, Martin, and Priscila López. 2011. "The World's Technological Capacity to Store, Communicate, and Compute Information." Science. vol. 332, no. 6025. April 1. pp. 60-65. • Hilbert, Martin, and Priscila López. 2012a. "Info Capacity| How to Measure the World’s Technological Capacity to Communicate, Store and Compute Information? Part I: Results and Scope." International Journal of Communication. vol. 6, pp. 956-979. [] • Hilbert, Martin, and Priscila López. 2012b. "Info Capacity| How to Measure the World’s Technological Capacity to Communicate, Store and Compute Information? Part II: Measurement Unit and Conclusions." International Journal of Communication. vol. 6, pp. 936-955. [] • Koomey et al. 2002. "Sorry, wrong number: The use and misuse of numerical facts in analysis and media reporting of energy issues." In Annual Review of Energy and the Environment 2002. Edited by R. H. Socolow, D. Anderson and J. Harte. Palo Alto, CA: Annual Reviews, Inc. (also LBNL- 50499). pp. 119-158. • Koomey, Jonathan, Kenneth G. Brill, W. Pitt Turner, John R. Stanley, and Bruce Taylor. 2007. A simple model for determining true total cost of ownership for data centers. Santa Fe, NM: The Uptime Institute. September. <> • Koomey, Jonathan. 2008. "Worldwide electricity used in data centers." Environmental Research Letters. vol. 3, no. 034008. September 23. <>. 32
  33. 33. References (continued) • Koomey, Jonathan G., Stephen Berard, Marla Sanchez, and Henry Wong. 2011. "Implications of Historical Trends in The Electrical Efficiency of Computing." IEEE Annals of the History of Computing. vol. 33, no. 3. July-September. pp. 2-10. [] • Koomey, Jonathan. 2011. Growth in data center electricity use 2005 to 2010. Oakland, CA: Analytics Press. August 1. [] • Koomey, Jonathan G. 2012. The Economics of Green DRAM in Servers. Burlingame, CA: Analytics Press. November 2. [] • Koomey, Jonathan G., H. Scott Matthews, and Eric Williams. 2013. "Smart Everything: Will Intelligent Systems Reduce Resource Use?" The Annual Review of Environment and Resources.vol 38. October. pp. 311-343. []. • Masanet, Eric R., Richard E. Brown, Arman Shehabi, Jonathan G. Koomey, and Bruce Nordman. 2011. "Estimating the Energy Use and Efficiency Potential of U.S. Data Centers." Proceedings of the IEEE. vol. 99, no. 8. August. • Masanet, Eric, Arman Shehabi, and Jonathan Koomey. 2013. "Characteristics of Low-Carbon Data Centers." Nature Climate Change. July. Vol. 3, No. 7. pp. 627-630. [] • Malmodin, Jens, Dag Lundén, Åsa Moberg, Greger Andersson, and Mikael Nilsson. 2013. "Life cycle assessment of ICT networks–carbon footprint and operational electricity use from the operator, national and subscriber perspective." Submitted to The Journal of Industrial Ecology. March 8. • Traub, Todd. 2012. "Wal-mart used technology to become supply chain leader." In Arkansas Business. July 2. [] • Weber, Christopher, Jonathan G. Koomey, and Scott Matthews. 2010. "The Energy and Climate Change Impacts of Different Music Delivery Methods." The Journal of Industrial Ecology. vol. 14, no. 5. October. pp. 754–769. [] 33