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A Time Series-based Approach for Power Management in Mobile Processors and Disks
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Transcript of A Time Series-based Approach for Power Management in Mobile Processors and Disks
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 driveEnergy 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)
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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]/ Th: hit ratea: arrival rateRdiff: rotational latency difference
Choose the lowest RPM level satisfying (ûi- ûmax) / ûmax ≤ threshold
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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
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Contents Introduction Time Series based Power Management
Utilization Measurement Prediction Model Speed Setting Strategy
Implementation Evaluation Summary
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TS-DVFSUp to 38.6% energy saving against LongRun
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TS-DRPMUp 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