utility-function-driven energy-efficient cooling in data centers
DESCRIPTION
Utility-Function-Driven Energy-Efficient Cooling in Data Centers. Authors: Rajarshi Das, Jeffrey Kephart , Jonathan Lenchner , Hendrik Hamamn IBM Thomas J. Watson Research Center Presented by: Shivashis Saha University of Nebraska-Lincoln. Outline. Introduction Related Work - PowerPoint PPT PresentationTRANSCRIPT
Utility-Function-Driven Energy-Efficient Cooling in Data Centers
Authors:Rajarshi Das, Jeffrey Kephart, Jonathan Lenchner, Hendrik Hamamn
IBM Thomas J. Watson Research Center
Presented by:Shivashis Saha
University of Nebraska-Lincoln
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Outline
• Introduction• Related Work• Data Center Energy Balance• Utility Functions– Multiplicative utility functions– Additive utility functions
• Experiments• Conclusion
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Introduction• Data center energy management– “50% of existing data centers will have insufficient
power and cooling within two years” – “Power is the second-highest operating cost in
70% of all data centers”– “Data centers are responsible for the tens of
millions of metric tons of carbon dioxide emissions annually --- more than 5% of the total global emissions”
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Introduction• Why use autonomic computing?– Large, difficult to manage, complex– Management problem is both qualitatively similar
to and quantitatively harder than that of managing IT alone.
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Contributions
• Apply utility functions to save energy– Tradeoff between energy and temperature– Control parameters:• Fan speed• On/off states of individual Computer Room Air
Conditioning (CRAC)
• Proposed model show 12% reduction in energy without violating temperature contraints
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Related Work
• Saving more energy is not good if administrator does not want that!– Proposed model is flexible
• Apply computational fluid dynamics modeling to complex data center environments
• Temperature aware workload placement based on inlet temperature or heat recirculation
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Data Center Energy Balance• PDC, power to run data center is split using
switch gear equipment into:– Path to power the IT equipments– Path to power the supporting equipments
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Data Center Energy Balance• The support path may include– Power for pumping coolant to and from CRACs to
the chiller and to and from the chiller to the cooling tower
• Power path for IT equipments include– Conversion loss due to the uninterruptible power
supply (UPS) systems– Losses associated with the power distribution PPDU
– The UPS systems are located outside the raised floor area
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• The total power on the floor:
• PIT is the power consumed by the IT equipments• Total CRAC fan power and CDU pump power:
• The relation between fan power PCRACi and relative fan speed Θi
Data Center Energy Balance
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Data Center Energy Balance
• Under steady state condition, the total raised floor power equal to the total cooling power
– The reduced fan speed reduces the air flow:
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Data Center Energy Balance
• All raised floor power needs to be cooled by the chilling system, which required power for refrigeration
– COP: the coefficient of performance of the chiller system (assume, average COP = 4.5)
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Data Center Energy Balance
• Reducing CRAC fan speeds, the fan power is reduced
• This reduces both the raised floor power and the power needed from chiller system
• However, reducing fan speed also increases the server inlet temperature
A tradeoff between energy consumption and the temperature!!!
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Utility Functions
• Data center operators responsible for the physical environment tend not to be concerned about application level performance, e.g. performance, availability, or security
• They are more concerned about cost, energy, temperature, and hardware lifetimes
• There are two CRAC units, whose fan speeds are Θ1 and Θ2
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Utility Functions• Multiplicative utility functions
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Utility Functions
The previous utility function is very harsh!
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Utility Functions• Additive utility functions
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Experiments
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Experiments
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Experiments
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Experiments
Each CRAC was:1. Turned off2. Turned on at lowest speed (60%)3. Turned on at max speed (100%)
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Experiments
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Experiments• Snorkels were placed
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Experiments
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Conclusion
• Use of utility functions in data centers• Total reduction of energy consumption by 14% • Dynamic aspects of utility functions are not yet
considered• Investigation of techniques combining dynamic
workload scheduling with dynamic workload migration
Thanks!