Why predictive modeling is
essential for managing a
modern computing facility
Jonathan G Koomey, Ph.D.
http://www.koomey
R...
Understanding systems
2
The business problem
•  Data centers deliver computing services
that generate business value (i.e., profits)
•  Decisions ...
The data center problem
•  Facilities are built using an estimate of
compute capacity that is never realized
•  IT deploym...
Capacity fragments over time
5
The actual IT configuration will differ from the design assumptions. These differences will...
My focus today
•  What is a model?
– Uses of models
– Making a model
•  Why predictive modeling is essential for
avoiding ...
“An explicit model is a laboratory
for the imagination.”
–Anthony Starfield et al., How to Model It.
7
The Bay Model, Sausalito, CA
http://www.spn.usace.army.mil/Missions/Recreation/BayModelVisitorCenter.aspx
8
Everyone uses models, most badly
•  Usually informal models
•  Intuitive but not necessarily accurate
– Ignoring physics a...
Uses of formal models
•  Organize
– thinking
– data
– assumptions
– terminology
– communication between teams
•  Learn abo...
Making a model
•  Understand first principles
– Key drivers
– Functional relationships
•  Formalize using equations or phy...
Accurate calibration requires…
•  Real-time measurements
•  Comparison of model results to
measurement
•  Understanding of...
Real measurements needed!
13
Data centers are complex
systems
≠
14
http://www.fatcow.com/data-center-photos http://www.dell.com
Same equipment, different locations
15
Source: Future Facilities
Key data center issues
•  Constraints
– Reliability
– Power
– Cooling
– Space
– Networking
•  Interdependencies between
– ...
A complete model of a data center
should include…
•  Characteristics of equipment
– Physical dimensions and location
– Ope...
An accurate model also requires
•  Real-time measurement (i.e., DCIM) of
– Temperature
– Air flows
– Power use
•  Periodic...
and all of these things need to
be tracked in real time for the
life of the facility!
19
Equinix case study
20
Characteristics of Equinix facility
•  Case study, Spring 2013
•  Colocation facility in the SF Bay Area
•  Floor 1, model...
Recapturing lost capacity
22
Source: Future Facilities
Predictive IT deployment
23
•  How can Equinix
identify void
capacity for
clients?
•  Void capacity can
be reclaimed!
•  S...
Recapture lost capacity
24
Conclusions
•  Data centers are complex systems, changing
constantly over time
–  Like a game of Tetris
–  Fragmentation l...
References
•  Koomey, Jonathan, Kenneth G. Brill, W. Pitt Turner, John R. Stanley, and Bruce Taylor.
2007. A simple model ...
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Why predictive modeling is essential for managing a modern computing facility

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This talk, given at Data Center Dynamics on July 12, 2013, summarizes the importance of predictive modeling to capturing lost cooling and power capacity in the data center. It also describes some results from a recent case study Future Facilities did at an Equinix data center in the Bay area.

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Why predictive modeling is essential for managing a modern computing facility

  1. 1. Why predictive modeling is essential for managing a modern computing facility Jonathan G Koomey, Ph.D. http://www.koomey Research Fellow, Steyer-Taylor Center for Energy Policy and Finance, Stanford University Data Center Dynamics San Francisco, CA July 12, 2013 1
  2. 2. Understanding systems 2
  3. 3. The business problem •  Data centers deliver computing services that generate business value (i.e., profits) •  Decisions about IT deployment over the facility life almost never take business value fully into account, because of – siloed departments and budgets – misplaced incentives – imperfect foresight 3
  4. 4. The data center problem •  Facilities are built using an estimate of compute capacity that is never realized •  IT deployment decisions after construction are almost never according to plan •  The result: lost capacity due to fragmentation, resulting in stranded capex and high cost per computation 4
  5. 5. Capacity fragments over time 5 The actual IT configuration will differ from the design assumptions. These differences will fragment space, power, cooling & networking resources, and ultimately, limit data center capacity. Source: Future Facilities
  6. 6. My focus today •  What is a model? – Uses of models – Making a model •  Why predictive modeling is essential for avoiding stranded capex in data centers •  Case study: Predictive modeling for Equinix 6
  7. 7. “An explicit model is a laboratory for the imagination.” –Anthony Starfield et al., How to Model It. 7
  8. 8. The Bay Model, Sausalito, CA http://www.spn.usace.army.mil/Missions/Recreation/BayModelVisitorCenter.aspx 8
  9. 9. Everyone uses models, most badly •  Usually informal models •  Intuitive but not necessarily accurate – Ignoring physics and interdependencies – Ignoring effects of actions on lost capacity and business value •  Need to be more formal! 9
  10. 10. Uses of formal models •  Organize – thinking – data – assumptions – terminology – communication between teams •  Learn about complex systems – Intuition usually isn’t enough! •  Test alternative choices to aid planning 10
  11. 11. Making a model •  Understand first principles – Key drivers – Functional relationships •  Formalize using equations or physical structures •  Test against reality – measure and calibrate •  Then (and only then) use model to test alternatives! 11
  12. 12. Accurate calibration requires… •  Real-time measurements •  Comparison of model results to measurement •  Understanding of physical reasons for differences •  Adjustment of model parameters, accounting for physical reality (can’t just hard wire results!) 12
  13. 13. Real measurements needed! 13
  14. 14. Data centers are complex systems ≠ 14 http://www.fatcow.com/data-center-photos http://www.dell.com
  15. 15. Same equipment, different locations 15 Source: Future Facilities
  16. 16. Key data center issues •  Constraints – Reliability – Power – Cooling – Space – Networking •  Interdependencies between – Constraints – Business objectives 16
  17. 17. A complete model of a data center should include… •  Characteristics of equipment – Physical dimensions and location – Operating characteristics (e.g., utilization) – Power use/efficiency curves – Equipment and building level air flows •  Characteristics of the physical space – #, type, capacity, and location of vents/fans – Obstructions (e.g., stray boxes and cabling) – Modifications in the envelope 17
  18. 18. An accurate model also requires •  Real-time measurement (i.e., DCIM) of – Temperature – Air flows – Power use •  Periodic calibration to reflect changed conditions over time •  Performance and financial metrics to judge progress 18
  19. 19. and all of these things need to be tracked in real time for the life of the facility! 19
  20. 20. Equinix case study 20
  21. 21. Characteristics of Equinix facility •  Case study, Spring 2013 •  Colocation facility in the SF Bay Area •  Floor 1, modeled white space: 8,750 sq ft •  Total facility floor space: 42,000 sq ft. •  Details on infrastructure – 2 ft raised floor airflow delivery – 42” false ceiling return plenum. – 12 AHU’s N+2 redundancy 21
  22. 22. Recapturing lost capacity 22 Source: Future Facilities
  23. 23. Predictive IT deployment 23 •  How can Equinix identify void capacity for clients? •  Void capacity can be reclaimed! •  Simulating IT changes prior to installation will: –  Increase thermal resilience –  Enable additional cabinet power to be utilized Managing IT Deployment Projected Configuration From Current Source: Future Facilities
  24. 24. Recapture lost capacity 24
  25. 25. Conclusions •  Data centers are complex systems, changing constantly over time –  Like a game of Tetris –  Fragmentation leads to lost capacity •  Monitoring and measurement are not enough! •  Much lost capacity can be reclaimed using predictive modeling and state of the art tools, with support of DCIM measurements •  Don’t turn knobs without knowing the likely results! 25
  26. 26. References •  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. <http://www.uptimeinstitute.org/> •  Koomey, Jonathan. 2008. "Worldwide electricity used in data centers." Environmental Research Letters. vol. 3, no. 034008. September 23. <http://stacks.iop.org/ 1748-9326/3/034008>. •  Koomey, Jonathan. 2008. Turning Numbers into Knowledge: Mastering the Art of Problem Solving. 2nd ed. Oakland, CA: Analytics Press. [http://www.analyticspress.com] •  Koomey, Jonathan. 2011. Growth in data center electricity use 2005 to 2010. Oakland, CA: Analytics Press. August 1. <http://www.analyticspress.com/datacenters.html> •  Stanley, John, and Jonathan Koomey. 2009. The Science of Measurement: Improving Data Center Performance with Continuous Monitoring and Measurement of Site Infrastructure. Oakland, CA: Analytics Press. October 23. <http://www.analyticspress.com/ scienceofmeasurement.html> •  Starfield, Anthony M., Karl A. Smith, and Andrew L. Bleloch. 1990. How to Model It: Problem Solving for the Computer Age. New York, NY: McGraw-Hill, Inc. 26

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