Energy aware real time systems
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Energy Aware Real-Time Systems. G. Sudha Anil Kumar Real Time Computing and Networking Laboratory Department of Electrical and Computer Engineering Iowa State University CprE 545 class presentation. Real-Time System: Characteristics. Real-Time Guarantees Meeting deadlines Fault Tolerance

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Energy aware real time systems

Energy Aware Real-Time Systems

G. Sudha Anil Kumar

Real Time Computing and Networking Laboratory

Department of Electrical and Computer Engineering

Iowa State University

CprE 545 class presentation


Real time system characteristics

Real-Time System: Characteristics

  • Real-Time Guarantees

    • Meeting deadlines

  • Fault Tolerance

    • Tolerating faults

  • Quality of Service

    • Acceptable quality of service

  • Energy Consumption

    • Minimize overall energy consumption

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Real time system

Real Time system

Energy

Quality

Fault tolerance

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Fault tolerance vs quality

Fault Tolerance vs. Quality

  • Imprecise Computation technique

    • Trading off quality for fault tolerance

  • (m, k)-firm deadline task model

    • Trading off quality for scheduling flexibility

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Imprecise computation ic

Imprecise Computation (IC)

Normal Task

Ci

Mandatory

Optional

Mi

Oi

Imprecise Computation task

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Ic relevant applications

IC: Relevant Applications

  • Image Processing: Fuzzy image in time are better than too late perfect image

  • Tracking: Rough estimate of target location in time is better than too late accurate location data.

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M k firm deadline tasks

(m, k)-firm deadline tasks

Task (T): C = 1; P = 2;

0

2

4

6

8

10

Time

M = 2; K = 3;

0

2

4

6

8

10

Time

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M k relevant applications

(m, k): Relevant Applications

  • Radar tracking: A few well spaced deadlines can be tolerated

  • Automobile control, multi-media streaming, etc..

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Real time system1

Real Time system

Energy

Quality

Fault tolerance

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Energy vs quality

Energy vs. Quality

  • Conflicting Design Objectives

    • Energy savings

    • Quality of Service

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Organization of the presentation

Organization of the presentation

  • Energy issues in RT-Embedded systems

  • Dynamic Voltage Scaling (DVS)

  • RT-DVS schemes

  • Energy aware RT-DVS for IC and (m, k) tasks

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Energy consumption in rt es

Energy consumption in RT-ES

  • Energy consumption is an important issue in RT-embedded systems like:

    • Laptops, PDAs.

    • Digital camcorders, cellular phones

    • portable medical devices.

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Important facts 1

Important Facts (1)

  • The peak computing rate needed is much higher than the average throughput that must be sustained

  • High performance is needed only for a small fraction of time, while for the rest of time, a low-performance, a low-power processor would suffice

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Workload profile

Workload Profile

Work load

Peak Computing Rate is needed

Average rate would suffice

Time

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Important facts 2

Important Facts (2)

CMOS based processors

Varying voltage and frequency we can reduce the energy consumption

Power (P) αV2 .f

V αf

Energy (Ei) αcci .f2

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Variable voltage processors

Variable Voltage Processors

  • Modern processors operate at multiple frequency (and voltage) levels.

    • Crusoe Processor: Transmeta Corporation

    • PowerNow! Technology: AMD

    • Intel XScale: Intel

  • Higher the frequency level higher the energy consumption

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Dynamic voltage scaling dvs

Dynamic Voltage Scaling (DVS)

  • DVS scales the operating voltage of the processor along with the frequency.

  • Since the energy consumption is proportional to V2 , DVS can potentially provide a very large energy savings.

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Dvs example

DVS-example

  • Consider a task with a computation time 20 units.

  • Energy of Ti without DVS

    • E1 = K * 20 * F2.

  • Energy of Ti with DVS

    • E2 = K * 20 * (F/2)2.

  • Clearly, E2 = (E1)/4.

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Energy time tradeoffs

Energy-Time Tradeoffs

60

40

Energy Savings

20

10

Time

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Energy aware rt scheduling objectives

Energy aware RT-scheduling: objectives

  • Minimizing energy consumption

  • Maximize the quality

  • Meeting the deadlines

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Energy aware rts techniques

Energy aware RTS Techniques

  • OS Level Energy Management

    • Inter-task DVS

  • Compiler Level Energy Management

    • Intra-task DVS

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    Intra vs inter task dvs

    Intra vs. Inter-task DVS

    • Inter-task DVS scheme: Voltage scheduling is done on a task by task basis.

    T3

    T1

    T2

    • Intra-task DVS scheme: Voltage scheduling is done within a task boundary.

      • Each task is modeled as a control flow graph.

    Voltage scheduling points

    T3

    T1…

    …T1

    T2…

    …T2

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    Energy aware rt scheduling ic tasks

    Quality

    Oi

    Energy Aware RT-Scheduling IC tasks

    System Model

    • OS level DVS

    • Inter-task DVS

    Each periodic task is specified by :

    Ci, Pi, Mi, Oi

    Energy budget per hyper-period:

    Eb

    Mi

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    Energy aware rt scheduling of ic tasks 8

    Energy aware RT-Scheduling of IC tasks [8]

    • Goal:

      • To schedule a set of Imprecise Computation tasks

    • Objective:

      • maximize the quality

    • Constraints:

      • without exceeding the deadlines

      • Without exceeding the total energy available

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    Optimal solution 8

    Optimal Solution [8]

    Find the Minimum energy

    frequencies settings of each task

    Find the Maximum quality solution

    With the above frequency settings

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    Minimum energy frequency settings

    Minimum energy frequency settings

    • Theorem: All tasks will execute at the same frequency in the minimum-energy solution

      • Due to the concave nature of the energy function

      • The above theorem is proved using rigorous mathematical tools.

      • The intuition follows……

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    Energy function characteristics

    Energy function characteristics

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    Example

    Example

    • Consider two tasks:

      • T1 = (3, 12) and T2 = (3,12)

    +ΔE2

    Energy

    T2 @ f = 0.7

    -ΔE1

    f = 0.5

    T1 @ f = 0.4

    Time

    0

    7.5

    12

    ΔE1 < ΔE2

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    Example minimum energy frequency settings

    Example: Minimum energy frequency settings

    • Consider two tasks:

      • T1 = (3, 12) and T2 = (3,12)

    Energy

    f = 0.5

    T1 @ f = 0.5

    T2 @ f = 0.5

    6.0

    0

    12

    Time

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    Calculating the minimum energy frequency

    Calculating the minimum energy frequency

    • Given: energy budget, Eb per LCM

    • We know: power, P = k * f3

    • Solve for fop: k * fop3 = Eb / LCM

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    Reduced problem 8

    Reduced Problem [8]

    • Goal:

      • To schedule a set of Imprecise Computation tasks

    • Objective:

      • maximize the quality

    • Constraints:

      • without exceeding the deadline

      • Without exceeding the total energy available

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    Reducing the problem

    Reducing the problem

    Ti = (Ci, Pi, Mi, Oi)

    Ti = (Ci/fop, Pi, Mi/fop, Oi/fop)

    Ti = (C’i, Pi, M’i, O’i)

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    Optimal solution to the reduced problem

    Optimal Solution to the reduced problem

    • Theorem: There exists an optimal solution to the reduced problem where the optional parts of a task Ti receive the same service time at every instance

    • The above theorem is proved using rigorous mathematical tools.

    • The intuition follows….

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    Optimal solution with equal optional service times

    Optimal solution with equal optional service times

    M11

    O11

    M21

    O21

    Both satisfy constraints

    Oi1 = (O11 + O21)/2

    M11

    O11

    M21

    O21

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    Algorithm for linear quality functions

    Algorithm for linear quality functions

    • Step1: Sort all the tasks in the order of (ki/bi), where bi is the number of instances of Ti in LCM.

    • Step 2: Allocate maximum possible slack to the task with largest (ki/bi)

    Mi

    Quality

    Qi = ki * ti

    Oi

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    The entire procedure

    The entire procedure

    Find the optimal frequency

    which isthe same forall tasks

    Find the Maximum quality solution

    by determining the optional service times

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    The m k firm guarantee task model

    The (m,k) firm guarantee Task Model

    • Energy aware (m,k) Problem:

      • (m, k)-firm deadlines

      • Minimize energy consumption

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    Static algorithm by gang et al 1

    Static algorithm by Gang et al. [1]

    • Assumptions:

      • Each task is specified by: (Pi,Di,Ci,mi,ki).

      • Processor provides two voltage/frequency modes (high and low).

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    Static algorithm contd

    Static algorithm (contd..)

    • Algorithm:

      • Sort all the tasks as per their utilizations.

      • Test the task set for the schedulability at the High Frequency Mode.

      • Considers the next highest utilization task, and checks (with respect to schedulability) if it can be slowed down.

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    Static algorithm drawbacks

    Static Algorithm: drawbacks

    • Algorithms’ run time increases exponentially with the number of voltage levels.

    • Does not capture the energy-value tradeoffs.

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    Conclusions

    Conclusions

    • Energy-Quality-Time tradeoff is an important issue in Embedded RTS.

    • There is a lot of scope to work in this area (e.g. better energy aware (m, k)-firm deadline task scheduling)

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    References

    References

    • [1] Quan, G., L. Niu and J. P. Davis, "Power Aware Scheduling for Real-Time Systems with (m,k)-Guarantee", Proceedings CNDS-04: Communication Networks and Distributed Systems Modeling and Simulation, The Society for Modeling and Simulation International, 2004.

    • [2] http://www.transmeta.com/crusoe/faq.html#8

    • [3] Real-Time Dynamic voltage scaling for Low-Power Embedded Operating Systems, P. Pillai and K. G. Shin, in ACM SOSP, pages 89-201, 2001.

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    References1

    References

    • [4] Intra-task Voltage Scheduling on DVS-Enabled Hard Real-Time Systems, D. Shin and J. kim, IEEE Design and Test of Computers, March 2001.

    • [5] Maximizing the System Value while Satisfying Time and Energy Constraints, Cosmin Rusu, Rami Melhem, Daniel Mossé; ,IBM Journal of R&D, vol 47, no 5/6, 2003

    • [6] Hard Real-Time scheduling for Low-energy using stochastic data and DVS Processors, Flavius Gruian, symposium on low power electronics and design, 2001.

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    References2

    References

    • [7] Scheduling with Dynamic Voltage/Speed Adjustment Using Slack Reclamation in Multi-Processor Real-Time Systems, D. Zhu, R. Melhem, and B. Childers, IEEE Trans. on Parallel & Distributed Systems, vol. 14, no. 7, pp. 686 - 700, 2003.

    • [8] C. Rusu, R. Melhem and D. Mossé, "Maximizing Rewards for Real-Time Applications with Energy Constraints", Accepted for publication in ACM Transactions on Embedded Computer Systems.

    • [9] R. Mishra, N. Rastogi, D. Zhu, D. Mosse, R. Melhem, "Energy Aware Scheduling for Distributed Real-Time Systems", Proc. of the International Parallel and Distributed Processing Symposium (IPDPS'03), Nice, France (April 2003).

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    Thank you

    Thank You!!

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