Energy-Efficient Soft Real-Time CPU Scheduling for Mobile Multimedia Systems

Authors: Wanghong Yuan, Klara Narhstedt

Appears in SOSP 2003

Presented by: Samuel Kim

Table of Contents

•About the Authors•Introduction•Algorithm•Implementation•Results•Related Works•Summary

About the Authors

•Wanghong Yuan▫B.S., M.S. Peking University▫Ph.D. University of Illinois at Urbana-

Champaign▫Software Engineer at Google

•Klara Nahrstedt▫Ph.D. University of Pennsylvania▫Professor at University of Illinois at

Urbana-Champaign

Table of Contents

•About the Authors•Introduction•Algorithm•Implementation•Experimental Results•Related Works•Conclusion

Introduction

•Multimedia Becoming A Standard in Mobile Computing▫Audio ▫Video ▫Data

•Goal on Mobile Systems▫Manage System Resources

Quality of Service - High Performance Energy Efficiency - Battery Life

Greater Control Over System Resources

•Hardware Adaptability▫CPU Voltage Scaling▫E = a•C•V•f2•t

•Software Adaptability▫Application Quality levels▫Statistical Performance Requirements

Soft Real-Time guarantees

How Do We Approach System Resource Management?

▫Adapt resources based on system layers

▫Most approaches in research adapt a single layer

▫Possible to adapt across multiple layers?

Adaptive Layers

Application

Operating System /Network

Hardware

Multiple Layer Adaptation Requires Coordination•Conflict Adapting Multiple System Layers

▫Scale down CPU▫Increase the application QoS

•Different Objectives▫Minimize Energy Consumption▫Maximize Quality/Performance

•Coordinate Objectives at a Higher Level

The Purpose of GRACE Framework: Cross-Layer Adaptation•Global Resource Adaptation via

CoopEration

Figures from S. Adve. “The Illinois GRACE Project: Global Resource Adaptation through CoopEration”, Workshop on Self-Healing Adaptive and Self-Managed Systems, 2002

• Global cooperation of resources

Ap

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Operating System

Network Protocols

Arc

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Coordinator

GRACE

• Adaptation over 1 or 2 layers

Application

Network Protocols

Operating System

Architecture, Hardware

Current Approaches

GRACE-OS: Enhanced CPU Scheduler

•Previous Methods▫Soft Real-Time (SRT) Scheduling▫Dynamic Voltage Scaling (DVS)

•GRACE-OS ▫DVS is integrated into the CPU Scheduler▫Continue to keep performance guarantees

of SRT Scheduling

Table of Contents

•About the Authors•Introduction•Design and Algorithm•Implementation•Experimental Results•Related Works•Conclusion

Design of GRACE-OS•Profiler

▫How to estimate cycle usage? Monitor CPU cycle usage of a task Estimate demand by online profiling

•SRT Scheduler▫How to allocate CPU Resources?

Allocate CPU cycles to task based on profiler•Speed Adapter

▫How to set CPU Speed/Voltage? Set CPU to minimum required speed based on

#cycles allocated

Algorithm: Profiler

•How to estimate the cycle usage?•Estimate based on statistical distribution

instead than instantaneous demand▫More stability in CPU speeds▫Meets performance requirements of SRT

•Profile during run-time

Algorithm: SRT Scheduler

•Determine which task to execute▫When and how long (# CPU cycles)

•Grace-OS is a stochastic scheduler▫Decide # cycles to allocate based on:

Performance requirement, p Demand distribution of task

▫F(C) = P[X ≤ C] ≥ p X, # cycles required for task C, # cycles allocated to task

Algorithm: Dynamic DVS•As cycle number increases, CPU

accelerates•Minimize energy consumption

▫Constraint: CPU period is less than period allocated for task

•Frequency a function of cycle count

Table of Contents

•About the Authors•Introduction•Algorithm•Implementation•Experimental Results•Related Works•Conclusion

Implementation

•Testbed▫HP Pavilion N5470 Laptop (Athlon

Processor)▫Red Hat Linux 7.2▫Modified Linux kernel 2.4.18 (GRACE-OS)

•Software Architecture of Implementation

Implementation

•System calls added to support SRT tasks▫ start_srt – start real-time mode▫ exit_srt – exit real-time mode▫ finish_job – tell scheduler that task finished job▫ set_budget – allocate cycles for task▫ set_dvspnt – set CPU speed in task’s speed schedule

•Modifying the process control block▫ 5 attributes

Table of Contents

•About the Authors•Introduction•Algorithm•Implementation•Experimental Results•Related Works•Conclusion

System Call Overhead

•System Calls: 900-1300 cycles •Multimedia Processing: 2x105 - 2x108

cycles▫0.0004% - 0.5% of cycles per job

Profiling and Estimation Overhead•Profiling Cost: 26-38 cycles•Overhead for online demand estimation is

high (0.1% - 100% of cycles per job)▫Demand estimation should be infrequent▫Stable models allow for infrequent

estimation

Figure: Cost of Demand Estimation

Speed Scaling Overhead

•Costs 8,000 to 16,000 cycles (~10-50 us)•Should be invoked infrequently (500 us in

GRACE-OS)•Speed change overhead should improve with

processor design

Stability of Demand Distribution

•Codec: mpgplaya) Cycle usage varies

greatly b) Demand distribution

remains stable

Stability of Demand Distribution (Other Codecs)

Efficiency of GRACE-OS

•Compare to other allocation schemes•Running Single Applications

▫Misses deadlines 0.3%-0.6%▫92% CPU busy time at lowest CPU speed▫53.4%-71.6% reduction in energy

•Running Multiple Applications▫Misses deadlines 4.9%▫83.8% CPU busy time at lowest CPU speed

CPU Usage for Multiple Applications

•Dynamic DVS spends more time in lowest CPU speed than other DVS schemes

Energy Efficiency of GRACE-OS

• toast and madplay – Low CPU demand•GRACE-OS savings limited by CPU

settings

Impact of Setting Performance p

•Normalized energy increases p = 0.5 to p = 0.95

•Fewer energy savings p = 0.95 to p = 1.0▫Need more CPU settings

Impact of p on Normalized Energy

Impact of Mixed Workload

•Extra allocation to extra best-effort applications increases energy consumption▫Less time for each application▫Increases total CPU demand

Impact of Mixed Workload

Table of Contents

•About the Authors•Introduction•Algorithm•Implementation•Experimental Results•Related Works•Conclusion

Related Works: Soft Real-Time Scheduling• Proportional Sharing

▫ A. Chandra, M. Adler, P. Goyal, and P. Shenoy. Surplus fair scheduling: A proportional-share CPU scheduling algorithm for symmetric multiprocessors. In Proc. of 4th Symposium on Operating System Design and Implementation, Oct. 2000.

• CPU Reservations▫ M. Jones, D. Rosu, and M. Rosu. CPU reservations & time constraints: Efficient,

predictable scheduling of independent activities. In Proc. of 16th Symposium on Operating Systems Principles, Oct. 1997.

• Real-Time Scheduling Algorithms▫ C. L. Liu and J. W. Layland. Scheduling algorithms for multiprogramming in a

hard real-time environment. JACM, 20(1):46–61, Jan. 1973.

• Stochastic Scheduling▫ K. Gardner. Probabilistic analysis and scheduling of critical soft real-time systems. PhD

thesis, Department of Computer Science, University of Illinois at Urbana-Champaign, 1999.

Related Works: Dynamic Voltage Scaling• General Purpose DVS based on Average CPU Utilization

▫ D. Grunwald, P. Levis, K. Farkas, C. Morrey III, and M. Neufeld. Policies for dynamic clock scheduling. In Proc. of 4th Symposium on Operating System Design and Implementation, Oct. 2000.

• Real Time DVS▫ P. Pillai and K. G. Shin. Real-time dynamic voltage scaling for low-

power embedded operating systems. In Proc. of 18th Symposium on Operating Systems Principles, Oct. 2001.

• Stochastic DVS▫ J. Lorch and A. Smith. Improving dynamic voltage scaling algorithms

with PACE. In Proc. of ACM SIGMETRICS 2001 Conference, June 2001.▫ F. Gruian. Hard real-time scheduling for low energy using stochastic

data and DVS processors. In Proc. Of Intl. Symp. on Low-Power Electronics and Design, Aug. 2001.

Table of Contents

•About the Authors•Introduction•Algorithm•Implementation•Experimental Results•Related Works•Conclusion

Conclusion• Pros

▫ Optimizes multiple layers of system resources ▫ Conserve energy while ensuring quality of service▫ Small overhead▫ Support for multiple tasks▫ Thorough testing

• Cons▫ Estimate energy savings without measurement▫ Testing limited to multimedia applications▫ Limited number of tests per codec

8 runs per test Discard largest and smallest values

▫ Limited CPU speed settings decreases energy savings