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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 Guarantees
- Meeting deadlines

- Fault Tolerance
- Tolerating faults

- Quality of Service
- Acceptable quality of service

- Energy Consumption
- Minimize overall energy consumption

anil@iastate.edu

Energy

Quality

Fault tolerance

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- 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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Normal Task

Ci

Mandatory

Optional

Mi

Oi

Imprecise Computation task

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

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Energy

Quality

Fault tolerance

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- Conflicting Design Objectives
- Energy savings
- Quality of Service

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- 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 is an important issue in RT-embedded systems like:
- Laptops, PDAs.
- Digital camcorders, cellular phones
- portable medical devices.

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- 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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Work load

Peak Computing Rate is needed

Average rate would suffice

Time

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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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- 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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- 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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- 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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60

40

Energy Savings

20

10

Time

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

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- OS Level Energy Management
- Inter-task DVS

- Intra-task DVS

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- 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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Quality

Oi

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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- 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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Find the Minimum energy

frequencies settings of each task

Find the Maximum quality solution

With the above frequency settings

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

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- 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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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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- 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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M11

O11

M21

O21

Both satisfy constraints

Oi1 = (O11 + O21)/2

M11

O11

M21

O21

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- 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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Find the optimal frequency

which isthe same forall tasks

Find the Maximum quality solution

by determining the optional service times

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

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- Assumptions:
- Each task is specified by: (Pi,Di,Ci,mi,ki).
- Processor provides two voltage/frequency modes (high and low).

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

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- 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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- [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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- [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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- [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).

anil@iastate.edu

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