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Optimizing Power and Energy

Optimizing Power and Energy. Lei Fan, Martyn Romanko. Motivation. 31% of TCO attributed to power and cooling Intermittent power constraints Renewable energy Grid balancing 20% - 30% utilization on average Green: good for the environment Green: saves money. Themes.

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Optimizing Power and Energy

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  1. Optimizing Power and Energy Lei Fan, Martyn Romanko

  2. Motivation • 31% of TCO attributed to power and cooling • Intermittent power constraints • Renewable energy • Grid balancing • 20% - 30% utilization on average • Green: good for the environment • Green: saves money

  3. Themes • Hybrid (hardware/software) optimizations • Dynamic DRAM refresh rates (Flikker) • Dynamic voltage/frequency scaling (MemScale) • Distributed UPS management • Power cycling (Blink) • Software optimizations • Dynamic adaptation (PowerDial)

  4. Flikker: Saving DRAM Refresh-power through Critical Data Partitioning • Partitioning of data into critical vs. non-critical • Partitioning of DRAM into normal vs. low refresh rates • Programming language construct • Allows marking of critical/non-critical sections • Primarily software with suggested hardware optimizations • OS and run-time support • Refresh rate optimizations

  5. Flikker

  6. MemScale: Active Low-Power Modes for Main Memory • Modern DRAM devices allow for static scaling • MemScale adds: • DVFS for MC; DFS for memory channels and DRAM devices • Policy based on power consumption and performance slack

  7. MemScale

  8. Managing Distributed UPS Energy for Effective Power Capping in Data Centers • Use of distributed UPSs to sustain peak power loads • Based on existing distributed UPS models • Larger batteries needed for longer peak spikes • Allows for more servers to be provisioned • Analysis of effect on battery lifetime • Argued benefit outweighed cost of extra batteries • Lacked detailed analysis on cooling costs

  9. Blink: Managing Server Clusters on Intermittent Power • Reducing energy footprint of data centers • Power-driven vs. workload driven • Blink: power-driven technique • Metered transitions between • High power active states • Low power inactive states

  10. Blink • Three policies • Synchronous: optimizes for fairness • Activation: optimizes for hit rate • Load-proportional: both • Unknown effects of power cycling on component lifetime

  11. PowerDial: Dynamic Knobs for Power-Aware Computing • When is this applicable for a program? • QoS (accuracy) vs. power/performance tradeoff • Subject to system fluctuations • Dynamic tuning of program parameters • Adaptable to fluctuations in power/load • Determines control variables • Application Heartbeats framework provides feedback • Automatic insertion of API calls

  12. PowerDial

  13. Discussion, Questions?

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