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Cloud Data Center/Storage Power Efficiency Solutions

Cloud Data Center/Storage Power Efficiency Solutions. Junyao Zhang. Current Storage Energy Efficiency Solutions: Tradeoff Energy/Performance. Multi-speed Disk: DRPM CPU has Dynamic Voltage and Frequency Scaling (DVFS): can we use this idea on storage systems?. Data Consolidation: MAID, PDC

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Cloud Data Center/Storage Power Efficiency Solutions

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  1. Cloud Data Center/Storage Power Efficiency Solutions Junyao Zhang

  2. Current Storage Energy Efficiency Solutions: Tradeoff Energy/Performance • Multi-speed Disk: DRPM • CPU has Dynamic Voltage and Frequency Scaling (DVFS): can we use this idea on storage systems? • Data Consolidation: MAID, PDC • Skew data into partial disks/cache disks so that others can be shut down • Request Consolidation (Replication-based): EERAID, Diverted Access • Redirect requests to some replicas to spin down the others • VM Consolidation: SRCMap In what degree should we tradeoff energy/performance?

  3. Power Proportional • A standard metric proposed by Google [1]: • Computer components should consume energy in proportion to the system utilization. • Observation:

  4. Robust and Flexible Power-Proportional Storage • Strictly satisfy power proportional :

  5. Solution • Fine-grained power proportionality for one data-set

  6. More

  7. Read Performance

  8. Write Performance

  9. Handling Recovery • Bounded wake-up • Rebuild is power-proportional

  10. Near power-proportional(cnt.)

  11. Multi-data set: Fair Scheduling

  12. Degradation

  13. Sierra: Practical Power-proportionalilty for Data Center Storage • Power proportional layout with the concern of the following factors: • Fault-tolerance, Loading balance, Consistency, Good performance. • Three challenges: • Layout that allows significant power savings • Maintain read and write availability at the original levels • Predict the number of servers required at anytime

  14. C1: Power-aware Layout • Gear 1 (g=1): need 2 nodes (Gear group 0) to keep 1 the copy of all nodes • Gear 2 (g=2): 4 nodes

  15. C1: Power-aware Layout • Extending to three replicas and more: two options • Rack-aligned • Rotated

  16. C2: Distributed virtual log (DVL) • Aim: maintain read/write availability • Write: if secondaries not available, entering “logging mode”(write primary replicas to DVL and replicate DVL r-1 times )

  17. C3: Gear Scheduler • Aim: predict system load and schedules servers to power down or up accordingly. • Observation: predict hourly behavior based on historical records of this hour.

  18. Power Savings

  19. Performance • Response Time

  20. Conclusion • Power proportional is becoming an important metric for power/energy tradeoff • Rabbit proposed a idea-power proportional layout • Sierra considered factors such as: power, reliability, load balancing, consistency and etc.

  21. [1] L. A. Barroso and U. H¨olzle. The case for energy-proportional computing. Computer, 40(12):33–37, 2007. [2] E. Thereska, A. Donnelly, and D. Narayanan. Sierra: a powerproportional, distributed storage system. MSR-TR-2009-153, November 2009. [3] H. Amur, J. Cipar, V. Gupta, G. R. Ganger, M. A. Kozuch, and K. Schwan. Robust and flexible power-proportional storage. In SoCC, 2010.

  22. Thank you

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