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Optimal Fan Speed Control for Thermal Management of Servers

Optimal Fan Speed Control for Thermal Management of Servers. UMass-Amherst Green Computing Seminar September 21 st , 2009. Problem. Power consumption key challenge in data centers 1.5% U.S. consumption in 2006 (60b kwh) Every 1W server power  0.5-1W cooling power

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Optimal Fan Speed Control for Thermal Management of Servers

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  1. Optimal Fan Speed Control for Thermal Management of Servers UMass-Amherst Green Computing Seminar September 21st, 2009

  2. Problem • Power consumption key challenge in data centers • 1.5% U.S. consumption in 2006 (60b kwh) • Every 1W server power  0.5-1W cooling power • Increasing density == Need more cooling power • >76% data centers use blades (and rising) • Fan power can be 2000W or 23% in blade enclosures • Is airflow overprovisioned? Can we decrease consumption by regulating fan speed better? HP Confidential

  3. 10 fans total: 5 on top, 5 on bottom Fans pull air through blades and out Blade Enclosure Overview 5 on top 5 on bottom HP Confidential

  4. Basic Approach • Model the cooling/fan system • Need model relating blade temperatures to fan speed • Complex interactions with multiple variables • Develop smart fan speed controller • Use model as input to MIMO controller • Compare with existing integrated controller • Problem Definition • Minimize fan power consumption • Ensure blade temperatures don’t exceed threshold HP Confidential

  5. The Models • Power models are (kind of) easy • Blade power consumption  linear • Fan power consumption  cubic • Temperature models are hard • Complex zonal variations • Lack of sensors • Steady-state versus transient HP Confidential

  6. Temperature Models • Steady-state: use thermal resistance co-efficient R • CPU transfers heat to ambient air • Relationship between R and temp. change and power consumption • Relationship between R and fan speed • Solve for CPU temp. • Transient: use energy balance equation • Takes some time to transfer heat to air • Model as a first-order discrete time system • Validated the models though experiments • Models have 167 parameters!!! • Basically, 10 blades with 16 fans HP Confidential

  7. Fan Controller • Optimization problem is hard • Fan speed to power consumption relationship is non-linear • Transient model has non-linear relationship b/t CPU and fan speed • May not have feasible solution • Simplify the problem • Find minimum airflow needed at each blade (locally) • Solve the global problem using these airflows • Compare with Integrated Fan Controller • If temp goes up speed fan up • If temp goes down lower fan speed HP Confidential

  8. Results • Reduced fan power from 213W to 172W or 20% (Figure 9) • On-demand cooling of high utilization CPUs (Figure 10) • More stable temperatures (Figure 11) • Both controllers operate below thresholds (Figure 12) • Results conservative b/c of low-powered CPUs and over-provisioned for cooling (i.e., large enclosure) HP Confidential

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