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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