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ECE555 Topic Presentation Energy-efficient real-time scheduling Xing Fu 20 September 2008 PowerPoint PPT Presentation


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ECE555 Topic Presentation Energy-efficient real-time scheduling Xing Fu 20 September 2008 Acknowledge Dr. Jian-Jia Chen from ETH providing PPT Slides for IEEE RTAS 2007. Outline of Presentation. System-level Energy Management for Periodic Real-Time Tasks

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ECE555 Topic Presentation Energy-efficient real-time scheduling Xing Fu 20 September 2008

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Ece555 topic presentation energy efficient real time scheduling xing fu 20 september 2008

ECE555 Topic Presentation

Energy-efficient real-time scheduling

Xing Fu

20 September 2008

Acknowledge Dr. Jian-Jia Chen from ETH providing PPT Slides for IEEE RTAS 2007


Outline of presentation

Outline of Presentation

  • System-level Energy Management for Periodic Real-Time Tasks

  • On the Minimization of the Instantaneous Temperature for Periodic Real-Time Tasks

    Further reference:

    http://www.cs.pitt.edu/PARC/

    http://www.cs.utsa.edu/~dzhu/parc-2005.htm

    http://www.cs.pitt.edu/PARTS/publications.html


Outline of presentation1

Outline of Presentation

  • Why those two papers?

    Paper 1: Systematic results. Other related papers can be treated as special cases.

    Paper 2: A closely related field: temperature efficient real time scheduling.

  • What will be covered?

    1. Main concepts

    2. Key ideas

    3. Introduction of underlying mathematics if time allowed


Ece555 topic presentation energy efficient real time scheduling xing fu 20 september 2008

System-level Energy Management for Periodic Real-Time Tasks


What is system level energy management

What is System-level Energy Management?

  • A generalized power model which includes the static,frequency-independent active and frequency-dependentactive power components of the entire system,

  • Variations in the system power dissipation during the executionof different tasks

  • On-chip / off-chip workload characteristics of individualtasks.


Task and processor model

Task and Processor Model


Power model

Power Model


Derivation of energy efficient speed for a single task

Derivation of Energy-Efficient Speed for a Single Task


Energy efficient speed assignments for a task set

Energy-Efficient Speed Assignments for a Task Set

Minimize Energy

Guarantee Real Time


Energy lu

ENERGY-LU

  • Case 1: If energy efficient speed of a particular task is great than Smax, then in optimal solution, the speed of the task is Smax

  • Case 2: If ,

    speed of all tasks will be

  • Case 3: If ,then

  • In case 3, ENERGY-LU is formulated as


Solving energy lu

Solving ENERGY-LU

  • First Reduce to ENERGY-L problem by relaxing the last constrain of ENERGY-LU and solve ENERGY-L problem first.

  • Case 1: the solution of ENERGY-L problem is also the solution of ENERGY-LU.

  • Case 2: the solution of ENERGY-L problem is NOT the solution of ENERGY-LU.

  • If case 2, iteratively adjust solutions of ENERGY-L to solve ENERGY-LU.


Experiment results i

Experiment Results I


Dynamic reclaiming

Dynamic Reclaiming

  • Why Dynamic Reclaiming?

    In practice, many task instances (Jobs) complete without presenting their worst-case workload.

  • Dynamic Reclaiming is introduced to reclaim unused computation time to reduce the CPU speed while preserving feasibility.

  • Different scheduling scheme has its own Dynamic Reclaiming.


Dynamic reclaiming algorithm

Dynamic Reclaiming Algorithm

  • When a job is to be dispatched, it will get the unused computation time from completed higher priority jobs.

  • Use those time, reduce further CPU speed to save more power.

  • A supported data structure - queue is needed to store related information.


Experiment results ii

Experiment Results II


Conclusions

Conclusions

  • Addressed the problem of minimizing overall energy consumption of a real-time system, considering a generalized power model.

  • Formulated the problem as a convex optimization problem and derived an iterative, polynomial time solution using Kuhn-Tucker optimality conditions.

  • Provided a dynamic reclaiming extension for settings where tasks complete early.


On the minimization of the instantaneous temperature for periodic real time tasks

On the Minimization of the Instantaneous Temperature for Periodic Real-Time Tasks


Motivations for power saving

Motivations for Power Saving

  • Rapid Increasing of Power Consumption

    • The power consumption of processors increases dramatically.

  • Slow Increasing of the Battery Capacity

    • The battery capacity increases about 5% per year

  • Embedded Systems vs. Servers

    The reduction of power is also needed to cut the power bill off


Heat versus energy

Heat versus Energy

  • Energy

    • Minimize the accumulative energy

    • Prolong battery lifetime

    • Reduce execution cost

  • Heat

    • Minimize the instantaneous temperature

    • Prevent from overheating

    • Reduce packing cost


Cooling model

Cooling Model

  • Cooling is a complex phenomenon [Sergent and Krum 1998].

  • For tractability, a simple first-order approximation is needed.

  • key assumptions:

    1. Heat is lost via conduction

    2. Ambient temperature of the environment is constant.

  • This is likely a reasonable first-order approximation in some, but certainly not all, settings.


Cooling model1

Cooling Model

  • The ambient temperature is scaled to 0 Modeled by Fourier’s Law

  • Initialization


Problem definitions

CHIP

Proc.

Proc.

SMTAS

MMTAS

Problem Definitions

Generate a feasible schedule SC for a set of tasks T such that Ψ(SC) is minimized.

  • UTAS : uniprocessor temperature-aware scheduling problem

  • SMTAS : single-chip multiprocessor temperature-aware scheduling problem

  • MMTAS : multi-chip multiprocessor temperature-aware scheduling problem


Utas ideal processors

UTAS: Ideal Processors

  • Energy minimization

    • Executing at a constant speed in the earliest-deadline-first order is optimal in energy consumption minimization by Aydin et al. in RTSS 2001, where

    • E(SCEDF) · E(SC) for any feasible schedule SC, where SCEDF is to execute tasks by the above strategy.

  • Temperature minimization Schedule

    • Executingall of the tasks at a constant speed following the earliest-deadline-first (EDF) strategy


Utas ideal processors cont

UTAS: Ideal Processors (cont.)

  • The maximum temperature of schedule

  • The maximumtemperature of any feasible schedule

  • The ratio between the above two


Utas ideal processors cont1

UTAS: Ideal Processors (cont.)

This is an e-approximation algorithm which means the maximum temperature of the suboptimal scheme is at most e times as any optimal scheme.


Utas non ideal processors

UTAS: Non-Ideal Processors

  • The timing overhead in speed transition from si to sj

    is denoted by σi,j

  • When σi,j is negligible

    • Energy minimization

      Execute at two consecutive speeds of effective speed sT*so that the utilization is 100% is optimal

    • Temperature minimization

      Execute at two consecutive speeds of effective speed sT*so that the utilization is 100% and frequently change speeds

  • When σi,j is non-negligible

    • More complicated


Utas i j is negligible

speed

t

UTAS: σi,j is negligible


Utas i j is non negligible

UTAS: σi,j is non-negligible

speed

Speed transition overhead

t

When α = 1, β = 0.01, and σi,j = 1 for any 0 < i j ≤ H


Multiprocessor largest task first ltf

Multiprocessor: Largest-Task First (LTF)

M = 3

2

3

4

5

1

Loads (ci/pi)

  • Sort tasks in a non-increasing order of ci/pi

  • Assign tasks in a greedy manner to the processor with the smallest load

  • Execute tasks on a processor at the speed with 100% utilization

L1

1

L2

2

5

L3

3

4

Algorithm LTF is a 1.13-approximation algorithm

for energy efficiency.

Jian-Jia Chen, Heng-Ruey Hsu, Kai-Hsiang Chuang, Chia-Lin Yang, Ai-Chun Pang, and Tei-Wei Kuo, "Multiprocessor Energy-Efficient Scheduling with Task Migration Considerations", in ECRTS 2004.

Jian-Jia Chen, Heng-Ruey Hsu, and Tei-Wei Kuo, "Leakage-Aware Energy-Efficient Scheduling of Real-Time Tasks in Multiprocessor Systems", in RTAS 2006.


Smtas and mmtas

SMTAS and MMTAS

Applying Algorithm LTF for scheduling

  • (1.13e)-approximation for MMTAS

  • (2.371e)-approximation for SMTAS


Conclusions1

Conclusions

  • Analysis for the maximum instantaneous temperature for energy-efficient scheduling algorithms in uniprocessor and multiprocessor systems

    • e-approximation for uniprocessor scheduling on ideal processors

    • (1.13e)-approximation when multi processors are on a chip

    • (2.371e)-approximation when each processor is on an individual chip

    • designs for non-ideal processors


Comparison of two papers

Comparison of two papers

[1] Dynamic and Aggressive Power-Aware Scheduling Techniques for Real-Time Systems


Selected critiques i

Selected Critiques I

  • Maybe apply latest results from optimization community to derive Optimal solution.

    Example, Linear Matrix Inequality.

  • More accurate model of CPU cooling maybe investigated. Then new scheduling algorithms or feedback control system can be designed accordingly.


Selected critiques ii

Selected Critiques II

  • Optimizing other QoS parameters for power aware real time system.

    Examples: Thermal, fault tolerance, through-output.


Any question

Any Question?

Thank you!


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