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

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

[VarsamopoulosGupta 2008]

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]

Cooling Cost aware Scheduling [VarsamopoulosGupta-2008]

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 [15,15,15,15] kW with 2.1 m/s[30,5,5,20] kW with 1.7 m/s Temperature Contours Around Racks: [Yogendra Joshi, Georgia Tech/CERCS]

Think Globally, Act Locally Numerically Run simulations for a range of velocities Make a server heat load-Inlet T variation matrix Change in max. inlet T of servers Unit change in server loads S1S1 S2S2 SnSn S1S1 S2S2 SnSn Experimentally Vary the heat loads sequentially at servers for a chosen unit cell and monitor the max. server inlet T Advantage: The simulations run for different velocities are not required for the experimental approach. 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. A Matrix Where, server I load Minimum load (startup) Max. load (full utilization) Max. inlet T allowed by ASHRAE [Yogendra Joshi, Georgia Tech/CERCS] ]

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

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]

Thermal-aware Data Management [Adapted from VarsamopoulosGupta 2008]

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