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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

- Imprecise Computation technique
- Trading off quality for fault tolerance

- (m, k)-firm deadline task model
- Trading off quality for scheduling flexibility

Normal Task

Ci

Mandatory

Optional

Mi

Oi

Imprecise Computation task

- 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.

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

0

2

4

6

8

10

Time

M = 2; K = 3;

0

2

4

6

8

10

Time

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

- Conflicting Design Objectives
- Energy savings
- Quality of Service

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

- 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

Work load

Peak Computing Rate is needed

Average rate would suffice

Time

CMOS based processors

Varying voltage and frequency we can reduce the energy consumption

Power (P) αV2 .f

V αf

Energy (Ei) αcci .f2

- 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

- 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.

- 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.

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

- OS Level Energy Management
- Inter-task DVS

- Intra-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

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

- Goal:
- To schedule a set of Imprecise Computation tasks

- Objective:
- maximize the quality

- Constraints:
- without exceeding the deadlines
- Without exceeding the total energy available

Find the Minimum energy

frequencies settings of each task

Find the Maximum quality solution

With the above 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……

- 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

- 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

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

- Goal:
- To schedule a set of Imprecise Computation tasks

- Objective:
- maximize the quality

- Constraints:
- without exceeding the deadline
- Without exceeding the total energy available

Ti = (Ci, Pi, Mi, Oi)

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

Ti = (C’i, Pi, M’i, O’i)

- 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….

M11

O11

M21

O21

Both satisfy constraints

Oi1 = (O11 + O21)/2

M11

O11

M21

O21

- 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

Find the optimal frequency

which isthe same forall tasks

Find the Maximum quality solution

by determining the optional service times

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

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

- 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.

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

- 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)

- [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.

- [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.

- [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).