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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
- Tolerating faults
- Quality of Service
- Acceptable quality of service
- Energy Consumption
- Minimize overall energy consumption

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

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.

(m, k): Relevant Applications

- Radar tracking: A few well spaced deadlines can be tolerated
- Automobile control, multi-media streaming, etc..

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

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.

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

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

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

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.

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.

Energy aware RT-scheduling: objectives

- Minimizing energy consumption
- Maximize the quality
- Meeting the deadlines

Energy aware RTS Techniques

- OS Level Energy Management
- Inter-task DVS
- Compiler Level Energy Management
- Intra-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

Oi

Energy Aware RT-Scheduling IC tasksSystem Model

- OS level DVS

- Inter-task DVS

Each periodic task is specified by :

Ci, Pi, Mi, Oi

Energy budget per hyper-period:

Eb

Mi

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

Optimal Solution [8]

Find the Minimum energy

frequencies settings of each task

Find the Maximum quality solution

With the above 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……

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

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

Calculating the minimum energy frequency

- Given: energy budget, Eb per LCM
- We know: power, P = k * f3
- Solve for fop: k * fop3 = Eb / LCM

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

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….

Optimal solution with equal optional service times

M11

O11

M21

O21

Both satisfy constraints

Oi1 = (O11 + O21)/2

M11

O11

M21

O21

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

The entire procedure

Find the optimal frequency

which isthe same forall tasks

Find the Maximum quality solution

by determining the optional service times

The (m,k) firm guarantee Task Model

- Energy aware (m,k) Problem:
- (m, k)-firm deadlines
- Minimize energy consumption

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).

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.

Static Algorithm: drawbacks

- Algorithms’ run time increases exponentially with the number of voltage levels.
- Does not capture the energy-value tradeoffs.

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)

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.

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.

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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