NUS.SOC.CS5248

A Time Series-based Approach for Power

Management in Mobile Processors and Disks

X. Liu, P. Shenoy and W. Gong

Presented by Dai Lu

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Contents IntroductionTime Series based Power

Management Utilization Measurement Prediction Model Speed Setting Strategy

ImplementationEvaluationSummary

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IntroductionMultimedia 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

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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

DiskDRPM: Dynamic Rotations Per Minute Monitor disk request queue length On-disk cache impact not considered

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Why DRPM? Power- RPM relation

Ke: spindle motor voltage R: motor resistance ω: angular velocity

Similar to DVS for processors (P~fV2)

NUS.SOC.CS5248

Contents Introduction Time Series based Power

Management Utilization Measurement Prediction Model Speed Setting Strategy

Implementation Evaluation Summary

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New WorkLow 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

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TS-PM enabled OS kernel

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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 = Φ1 ũt-1+at

Φ1: Correlation coefficient

at: Error/ random shock

Sample Autocorrelation Function (SAC)

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Prediction Model Cont.

Estimated demand:

Estimated mean:

Estimated constant( SAC):

TS-DVFS: one AR(1) for every task

TS-DRPM: a single AR(1)

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Measuring utilization CPU

e: full-speed execution time

q: time quantum allocated to the task

Disk r: response time s: scaling factor

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Speed Setting Strategy TS-DVFS

Two level CPU setting Interval T

Subinterval within T

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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 ratea: arrival rateRdiff: rotational latency difference

Choose the lowest RPM level satisfying (ûi- ûmax) / ûmax ≤ threshold

NUS.SOC.CS5248

Contents Introduction Time Series based Power Management

Utilization Measurement Prediction Model Speed Setting Strategy

Implementation Evaluation Summary

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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

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Frequency and RPM Mapping

NUS.SOC.CS5248

Contents Introduction Time Series based Power Management

Utilization Measurement Prediction Model Speed Setting Strategy

Implementation Evaluation Summary

NUS.SOC.CS5248

TS-DVFSUp to 38.6% energy saving against LongRun

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TS-DRPM

Up to 20.3% saving against TPMperf (oracle)

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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.”

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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