1 / 21

A Time Series-based Approach for Power Management in Mobile Processors and Disks

A Time Series-based Approach for Power Management in Mobile Processors and Disks. X. Liu, P. Shenoy and W. Gong Presented by Dai Lu. Contents. Introduction Time Series based Power Management Utilization Measurement Prediction Model Speed Setting Strategy Implementation Evaluation

yale
Download Presentation

A Time Series-based Approach for Power Management in Mobile Processors and Disks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. A Time Series-based Approach for Power Management in Mobile Processors and Disks X. Liu, P. Shenoy and W. Gong Presented by Dai Lu

  2. Contents • Introduction • Time Series based Power Management • Utilization Measurement • Prediction Model • Speed Setting Strategy • Implementation • Evaluation • Summary

  3. Introduction • Multimedia applications prevalent on mobile devices • 3G/4G wireless network • Devices more and more powerful • Samsung SPH-V5400 hand phone is equipped with a 1.5 GB micro drive • Energy is a scarce resource

  4. Previous Work • CPU DVFS: Dynamic Voltage and Frequency Scaling • Infer task periodicity by work-tracking heuristic • Assume implicit deadlines for interactive applications • Only periodic applications; assumes applications tell OS their periods and work amount • Disk DRPM: Dynamic Rotations Per Minute • Monitor disk request queue length • On-disk cache impact not considered

  5. Why DRPM? • Power- RPM relation • Ke: spindle motor voltage • R: motor resistance • ω: angular velocity • Similar to DVS for processors (P~fV2)

  6. Contents • Introduction • Time Series based Power Management • Utilization Measurement • Prediction Model • Speed Setting Strategy • Implementation • Evaluation • Summary

  7. New Work • Low overhead • Prediction with simple statistical model in time series analysis • Processor + disk • TS-DVFS + TS-DRPM • Different CPU scaling factor for different tasks • Enable coexistence of MM and non-MM applications

  8. TS-PM enabled OS kernel

  9. Prediction Model • Box-Jenkins model in time series analysis • Assume a stationary process • Statistical properties (mean, variance) are essentially constant through time. • Firs-order autoregressive process (AR(1)) predictor • ũt = Φ1ũt-1+at • Φ1: Correlation coefficient • at:Error/ random shock • Sample Autocorrelation Function (SAC)

  10. Estimated demand: Estimated mean: Estimated constant( SAC): Prediction Model Cont. • TS-DVFS: one AR(1) for every task • TS-DRPM: a single AR(1)

  11. Measuring utilization • CPU e: full-speed execution time q: time quantum allocated to the task • Disk • r: response time • s: scaling factor

  12. Speed Setting Strategy • TS-DVFS • Two level CPU setting • Interval T • Subinterval within T

  13. Speed Setting Strategy • TS-DRPM • Performance slow-down • Pdiff[i] = a(1-h) × T × Rdiff[i] • Estimated utilization • ûi = û + Pdiff[i]/ T h: hit rate a: arrival rate Rdiff: rotational latency difference • Choose the lowest RPM level satisfying • (ûi- ûmax) / ûmax ≤ threshold

  14. Contents • Introduction • Time Series based Power Management • Utilization Measurement • Prediction Model • Speed Setting Strategy • Implementation • Evaluation • Summary

  15. Implementation • CPU • 300-677 MHz, Transmeta • Divide into 5 steps • Mapping scaling factor to frequency level • Disk • 3000-5400 RPM • Divide into 5 steps • Assumed power consumption level • Trace driven simulation with DiskSim

  16. Frequency and RPM Mapping

  17. Contents • Introduction • Time Series based Power Management • Utilization Measurement • Prediction Model • Speed Setting Strategy • Implementation • Evaluation • Summary

  18. TS-DVFS Up to 38.6% energy saving against LongRun

  19. TS-DRPM Up to 20.3% saving against TPMperf (oracle)

  20. Summary • Time series statistical model • TS-DVFS • TS-DRPM • Comments • General PM, no QoS measurement like deadline miss rate • Multiple rotational speed disk not commercially available • Increase the accuracy of profiling disk access patterns. • “Hit if response time < τ, otherwise miss.”

  21. References • Chameleon: Application Controlled Power Management with Performance Isolation, X. Liu and P. Shenoy, Technical report 04-26, Department of Computer Science, University of Massachusetts • Forecasting and time series: an applied approach 3rd ed, Bowerman and O’Connell, Duxbury, 1993 • Reducing disk power consumption in servers with DRPM, S. Gurumurthi, A. Sivasubramaniam and H. Franke, IEEE Computer, Dec 2003

More Related