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Power Aware Realtime Systems

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Matt Craven. Ruibin Xu. Daniel Mosse. Outline. Introduction to real-time systems. Introduction to power management. Speed adjustment in frame-based systems ... – PowerPoint PPT presentation

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Title: Power Aware Realtime Systems


1

Power Aware Real-time Systems
Daniel Mosse
A joint project with profs Rami Melhem Bruce
Childers Ahmed Amer Trying to rope
in everyone else
And students Nevine Abougazaleh Cosmin Rusu Dakai
Zhu Sameh Gobriel Matt Craven Ruibin Xu
2
Outline
  • Introduction to real-time systems
  • Introduction to power management
  • Speed adjustment in frame-based systems
  • Dynamic speed adjustment in multiprocessor
    environment
  • Tradeoff between energy consumption and
    reliability
  • Saving power in wireless networks

3
Real-time systems
Hard RT systems
Soft RT systems
Periodic
Aperiodic (frame-based)
non preemptive
non preemptive
preemptive
preemptive
parallel processors
uni-processor
4
REAL-TIME SYSTEMS
Hard Real Time system guarantee
deadlines
  • To guarantee deadlines, we need to know worst
    case execution times
  • Predictability need to know if deadlines may be
    missed

Soft Real Time system try to
meet deadlines
  • If a deadline is missed, there is a penalty
  • Provides statistical guarantees (probabilistic
    analysis)
  • Need to know the statistical distribution of
    execution times

Applications
Safety critical systems, control and command
systems, robotics, Communication, multimedia
5
Periodic, EDF scheduling
  • n tasks with maximum computation times Ci and
    periods Ti, for i1,…,n.

C2, T4
C3, T7
  • Dynamic priority scheduling (high priority to the
    task with earlier deadline)
  • All tasks will meet their deadlines if
    utilization is not more than 1.
  • Task set utilization is percentage of CPU used by
    all tasks
  • This example

6
Outline
  • Introduction to real-time systems
  • Introduction to power management
  • Speed adjustment in frame-based systems
  • Dynamic speed adjustment in multiprocessor
    environment
  • Tradeoff between energy consumption and
    reliability
  • Saving power in wireless networks

7
Power Management
  • Why What Power Management?
  • Battery operated Laptop, PDA and Cell phone
  • Heating complex Servers (multiprocessors)
  • Power Aware maintain QoS, reduce energy
  • How?
  • Power off un-used parts LCD, disk for Laptop
  • Gracefully reduce the performance
  • CPU dynamic power Pd CefVdd2f
    Chandrakasan-1992, Burd-1995
  • Cef switch capacitance
  • Vdd supply voltage
  • f processor frequency ? linear related to Vdd

8
Power Aware Scheduling
fmax
  • Static Power Management (SPM)
  • Static slack uniformly slow all tasks
    Weiser-1994, Yao-1995, Gruian-2000

Static Slack
D
E
T1
T2
idle
time
9
Power Management
  • Dynamic Power Management (DPM)
  • Dynamic slack non-worst execution 10
    Ernst-1997
  • DPM Krishna-2000, Kumar-2000, Pillai-2001,
    Shin-2001

fmax
Static Slack
D
E
T1
T2
idle
time
E/4
fmax/2
T1
T2
time
  • Multi-Processor
  • SPM length of schedule over deadline
  • DPM ???

10
Outline
  • Introduction to real-time systems
  • Introduction to power management
  • Speed adjustment in frame-based systems
  • Dynamic speed adjustment in multiprocessor
    environment
  • Tradeoff between energy consumption and
    reliability
  • Saving power in wireless networks

11
Speed adjustment in frame-based systems
Static speed adjustment
Assumption all tasks have the same deadline.
Smax
Smin
time
Select the speed based on worst-case execution
time,WCET, and deadline
12
Dynamic Speed adjustment techniques for linear
code
WCET
time
PMP
PMP
ACET
time
PMP
Speed adjustment based on remaining WCET
Note a task very rarely consumes its estimated
worst case execution time.
13
Dynamic Speed adjustment techniques for linear
code
Remaining WCET
time
PMP
PMP
Remaining time
time
PMP
Speed adjustment based on remaining WCET
14
Dynamic Speed adjustment techniques for linear
code
Remaining WCET
time
PMP
PMP
Remaining time
time
PMP
Speed adjustment based on remaining WCET
15
Dynamic Speed adjustment techniques for linear
code
time
PMP
PMP
time
Speed adjustment based on remaining WCET
16
Dynamic Speed adjustment techniques for linear
code
time
PMP
PMP
time
Speed adjustment based on remaining WCET
17
Dynamic Speed adjustment techniques for linear
code
WCET
WCE
WCE
WCE
time
Remaining time
ACET
Remaining av. ex. time
AV
AV
Smax
time
PMP
Smax
WCE
WCE
time
Speed adjustment based on remaining average
execution time
18
An alternate point of view
WCET
WCE
WCE
WCE
time
ACET
AV
WCE
time
PMP
stolen slack
Reclaimed slack
WCE
WCE
time
19
Dynamic Speed adjustment techniques for
non-linear code
PMP
p1
p3
p2
min
average
max
At a
PMP
  • Remaining WCET is based on the longest path
  • Remaining average case execution time is based on
    the branching probabilities (from trace
    information).

20
Greedy dynamic speed adjustment
correct
  • Giving reclaimed slack to the next ready task is
    not always a correct scheme

incorrect
PMP
  • Theorem A reclaimed slack has to be associated
    with a deadline and can be given safely to a task
    with an earlier or equal deadline.

21
aggressive dynamic speed adjustment
  • Theorem if tasks 1, …, k are ready and will
    complete before the next task arrival, then we
    can swap the time allocation of the k tasks. That
    is we can add stolen slack to the reclaimed slack

PMP
  • Experimental rule Do not be very aggressive and
    reduce the speed of a task below a certain speed
    (the optimal speed determined by Uav )

22
Outline
  • Introduction to real-time systems
  • Introduction to power management
  • Speed adjustment in frame-based systems
  • Dynamic speed adjustment in multiprocessor
    environment
  • Tradeoff between energy consumption and
    reliability
  • Saving power in wireless networks

23
Multiprocs and AND/OR Applications
  • Real-Time Application
  • Set of tasks
  • Single Deadline
  • Directed Acyclic Graph (DAG)
  • Comp. (ci, ai)
  • AND (0,0)
  • OR (0,0) probabilities

Ti
24
Slack Stealing
  • Shifting Static Schedule 2-proc, D 8


D
L0
Recursive if embedded OR nodes
25
Proposed Algorithms
  • Greedy algorithm, two phases
  • Off-line longest task first heuristic Slack
    stealing via shifting LSTi , EOi
  • On-line
  • Same execution order
  • Claim the slack LSTi ti (ti?LSTi)
  • Compute speed
  • Meet timing requirement Zhu-2001

26
Proposed Algorithms (cont)
  • Actual Running Trace left branch, Ti use ai

f
  • Possible Shortcomings
  • Number of Speed change (overhead)
  • Too greedy slow ? fast

27
Proposed Algorithms (cont)
  • Optimal for uniprocessor Single speed
  • Energy Speed Concave
  • Minimal Energy when all tasks SAME speed

28
References
  • Chandrakasan-1992 A. P. Chandrakasan and S.
    Sheng and R. W. Brodersen. Low-Power CMOS Digital
    Design. IEEE Journal of Solidstate Circuit, V27,
    N4, April 1992, pp 473--484
  • Burd-1995 T. D. Burd and R. W. Brodersen.
    Energy efficient cmos microprocessor design. In
    Proc. of The HICSS Conference, pages 288-297,
    Maui, Hawaii, Jan. 1995.
  • Weiser-1994 M. Weiser, B. Welch, A. J. Demers,
    and S. Shenker. Scheduling for reduced CPU
    energy. In Operating Systems Design and
    Implementation, pages 13-23, 1994
  • Yao-1995 F. Yao, A. Demers, and S. Shenker. A
    scheduling model for reduced cpu energy. In Proc.
    of The 36th Annual Symposium on Foundations of
    Computer Science, pages 374-382, Milwaukee, WI,
    Oct. 1995.
  • Gruian-2000 F. Gruian. System-Level Design
    Methods for Low-Energy Architectures Containing
    Variable Voltage Processors. The Power-Aware
    Computing Systems 2000 Workshop at ASPLOS 2000,
    Cambridge, MA, November 2000
  • Ernst-1997 R. Ernst and W. Ye. Embedded program
    timing analysis based on path clustering and
    architecture classification. In Proc. of The
    International Conference on Computer-Aided
    Design, pages 598604, San Jose, CA, Nov. 1997.
  • Krishna-2000 C. M. Krishna and Y. H. Lee.
    Voltage clock scaling adaptive scheduling
    techniques for low power in hard real-time
    systems. In Proc. of The 6th IEEE Real-Time
    Technology and Applications Symposium (RTAS00),
    Washington D.C., May. 2000.
  • Kumar-2000 P. Kumar and M. Srivastava,
    Predictive Strategies for Low-Power RTOS
    Scheduling, Proceedings of the 2000 IEEE
    International Conference on Computer Design VLSI
    in Computers and Processors
  • Pillai-2001 P. Pillai and K. G. Shin. Real-Time
    Dynamic Voltage Scaling for Low-Power Embedded
    Operating Systems, 18th ACM Symposium on
    Operating Systems Principles (SOSP?1), Banff,
    Canada, Oct. 2001
  • Shin-2001 D. Shin, J. Kim and S. Lee,
    Intra-Task Voltage Scheduling for Low-Energy Hard
    Real-Time Applications, IEEE Design and Test of
    Computers, March 2001
  • Zhu-2001 D. Zhu, R. Melhem, and B. Childers.
    Scheduling with Dynamic Voltage/Speed Adjustment
    Using Slack Reclamation in Multi-Processor
    RealTime Systems, RTSS'01 (Real-Time Systems
    Symposium), London, England, Dec 2001 152
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