Why predictive modeling is essential for managing a modern computing facility
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Transcript of Why predictive modeling is essential for managing a modern computing facility
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
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Understanding systems
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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
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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
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Capacity fragments over time
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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
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
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“An explicit model is a laboratory for the imagination.”
–Anthony Starfield et al., How to Model It.
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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 and interdependencies – Ignoring effects of actions on lost capacity and
business value • Need to be more formal!
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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
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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!
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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!)
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Real measurements needed!
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Data centers are complex systems
≠
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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 – Constraints – Business objectives
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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
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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
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and all of these things need to be tracked in real time for the
life of the facility!
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Equinix case study
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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
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Recapturing lost capacity
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Source: Future Facilities
Predictive IT deployment
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• 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
Recapture lost capacity
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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!
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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.
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