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Optimizing Power and Energy Management in Data Centers: Innovations and Techniques

This paper explores innovative techniques for optimizing power and energy management in data centers, addressing the critical issue that 31% of total cost of ownership (TCO) stems from power and cooling. We examine renewable energy integration, dynamic adaptations such as Flikker for DRAM refresh optimization, MemScale for low-power modes, and Blink for managing power in server clusters. We also discuss the implications of distributed UPS systems for peak power sustainability and the adaptive capabilities of PowerDial for balancing quality of service with power efficiency.

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Optimizing Power and Energy Management in Data Centers: Innovations and Techniques

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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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