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Thermal Aware Data Management in Cloud based Data Centers

NSF SEEDM workshop, May 2-3, 2011 . Thermal Aware Data Management in Cloud based Data Centers. Ling Liu College of Computing Georgia Institute of Technology. Thermal aware Computing Era. Power density increases Circuit density increases by a factor of 3 every 2 years

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Thermal Aware Data Management in Cloud based Data Centers

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  1. NSF SEEDM workshop, May 2-3, 2011 Thermal Aware Data Management in Cloud based Data Centers Ling Liu College of Computing Georgia Institute of Technology

  2. Thermal aware Computing Era • Power density increases • Circuit density increases by a factor of 3 every 2 years • Energy efficiency increases by a factor of 2 every 2 years • Effective power density increases by a factor of 1.5 every 2 years [Keneth Brill: The Invisible Crisis in the Data Center] • Maintenance/TCO rising • Data Center TCO doubles every three years • Three-year cost of electricity exceeds the purchase cost of the server • Virtualization/Consolidation is a 1-time/short term solution [Uptime Institute] • Thermal management corresponds to an increasing portion of expenses • Thermal-aware computing and management solutions becoming prominent • Increasing need for thermal awareness

  3. [VarsamopoulosGupta 2008]

  4. Thermal aware Task Scheduling in Data Centers • Given a total task C, how to divide it among N server nodes to finish computing task with minimal cooling energy cost ? • Self-Interference and cross-interference lead to the temperature rise of inlet air, should be minimized • Environment interference(room temperature) is not critical • Task scheduling in spatial domain [VarsamopoulosGupta 2008]

  5. Cooling Cost aware Scheduling [VarsamopoulosGupta-2008]

  6. Energy Saving by Dynamic Load Distribution Increasing the range of changes in the rack heat load • Heat load distribution of [30 kW, 5 kW, 5 kW, 20 kW] in the case study only needs 1.7 m/s (9,726 CFM) cooling air flow • It is 19% less than the uniform distribution needs • This could save ~$189,000 annually in typical real world data centers Temperature Contours Around Racks: [15,15,15,15] kW with 2.1 m/s [30,5,5,20] kW with 1.7 m/s [Yogendra Joshi, Georgia Tech/CERCS]

  7. Think Globally, Act Locally Make a server heat load-Inlet T variation matrix Run simulations for a range of velocities A Matrix Numerically Where, server I load Minimum load (startup) Max. load (full utilization) Max. inlet T allowed by ASHRAE Vary the heat loads sequentially at servers for a chosen unit cell and monitor the max. server inlet T Experimentally • Modifications: • Blocks of servers can be identified with same effect or no effect on the inlet T. • This will give insights on the sparsity of this matrix. • Reduce the computational work. Advantage: The simulations run for different velocities are not required for the experimental approach. [Yogendra Joshi, Georgia Tech/CERCS] ]

  8. 11 41 16 46 An Example VCRAC = 5m/s 37.5% decrease in Facilities Energy Consumption (For the same heat dissipation) 68% increase in allowed heat dissipation (For the same CRAC velocity) [Yogendra Joshi, Georgia Tech/CERCS]

  9. Pertinence of Thermal Maps in Data Center Management • Given an equipment utilization layout, find the temperature around the room • Create a collection of thermal maps or a function to “predict” thermal behavior of a task assignment • Use collection to decide on job placement (temporally and spatially) [VarsamopoulosGupta 2008]

  10. Thermal-aware Data Management [Adapted from VarsamopoulosGupta 2008]

  11. Thermal aware data management • Task profiling • CPU utilization, I/O activity etc • Equipment power profiling • CPU consumption, disk consumption etc • Heat recirculation modeling • Task management technologies Need for a comprehensive research framework

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