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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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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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