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
NUS.SOC.CS5248
Contents IntroductionTime Series based Power
Management Utilization Measurement Prediction Model Speed Setting Strategy
ImplementationEvaluationSummary
NUS.SOC.CS5248
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
NUS.SOC.CS5248
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
NUS.SOC.CS5248
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
NUS.SOC.CS5248
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
NUS.SOC.CS5248
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)
NUS.SOC.CS5248
Prediction Model Cont.
Estimated demand:
Estimated mean:
Estimated constant( SAC):
TS-DVFS: one AR(1) for every task
TS-DRPM: a single AR(1)
NUS.SOC.CS5248
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-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
NUS.SOC.CS5248
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
NUS.SOC.CS5248
Contents Introduction Time Series based Power Management
Utilization Measurement Prediction Model Speed Setting Strategy
Implementation Evaluation Summary
NUS.SOC.CS5248
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.”
NUS.SOC.CS5248
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