Analysis of Real-Time Multi-Modal FP-Scheduled Systems with Non-Preemptible Regions

Authors: Masud Ahmed (presenting)

Pradeep HettiarachchiNathan Fisher

COPARTS

Department of Computer ScienceWayne State University.

This research was supported by NSF, Wayne State University, and MathWorks Inc

COPARTS

2

Outline

Introduction: Adaptive Cruise Control (ACC) Systems Multi-Modal System (MMS)

Models: Sporadic Tasks Periodic Resources

Contributions: Protocol for a Mode-Change Determination of FP Schedulability Non-preemptive execution Usability

Future Work

introductionmodelscontributionsconclusion

COPARTS

3

Motivation: Real-Time ACC

Automotive ACC Systems Alerts driver if front vehicle is too close Use 77GHz Radar Transmission/Receiving

Design constraints Non-preemptive radar sweep Max Sweep Time Number of sweep

7µs and 2µs for 200m and 50m respectivelyHigher number implies better accuracy

Radar Sweep

introductionmodelscontributionsconclusion

COPARTS

4

Multi-Mode ACC

We consider Multi-Mode System for ACC Software mode

Exploit smaller sweep-time Use higher number of sweep Utilize low priority task to reclaim idle cycles Tasks (with non-preemptive region) scheduled

by FP Hardware mode

Enable shared platformFP-Schedulability analysis of a

MMS is computationally expensive.

No MMS support for non-preemption

introductionmodelscontributionsconclusion

COPARTS

5

Related Work

Santinelli et al. (2011), Stoimenov et al. (2009), and Phan et al. (2009, 2010).

Fu et al. (2010a, 2010b), Timmons and Scanlon (2009), and Kim (2007).

Tindell et al. (1996), Pedro and Burns (1998), and Real and Crespo (2004).

Multi-Modal Systems

Control Systems

Dedicated Platform

High Computation Time

Soft real-time systems.

introductionmodelscontributionsconclusion

COPARTS

6

Models: Sporadic Task

Sporadic Task

)(id

)(ie

)(ip

Execution

Period

Deadline

)()(

)()( )1,0max(),( i

i

ii e

p

dttdbf

DBF

t

Execution

RBF

)()(

)( ),( ii

i ep

ttrbf

introductionmodelscontributionsconclusion

COPARTS

7

Models: Periodic Resources

Capacity

Period-of-repetition

)(iPeriodic Resource (i)

Supply Bound

Function (SBF)

t

Supply )(i

introductionmodelscontributionsconclusion

COPARTS

8

Mode Definition

M(i)

),,(

}{

),(

)()()()(

1)()(

)()()(

iiii

nii

iii

pde

i

)(i

)(i

Hardware

Software

)(id

)(ie

)(ip

MMS Protocol for Mode-Change

introductionmodelscontributionsconclusion

COPARTS

9

Mode Change

tk

M(i) M(j) M(k)

ij jk

)()( jjN

mcrk=(M(j), M(k),tk)

ModeChangeReques

t

ModeChangeRequest

Transition

time

time

Transition

time

Old Mode New

Mode

New Mode

Old Mode

mcrk-1=(M(i), M(j),tk-1)

tk-1

MMS Protocol for Mode-Change

introductionmodelscontributionsconclusion

COPARTS

10

Different Tasks

tk-1 +

tkM(j) M(k)

ij

jk

Immediately Aborted Tasks A(ij)

Non-Aborted Tasks

Unchanged Tasks τ(ij)

X

X

MMS Protocol for Mode-Change

introductionmodelscontributionsconclusion

COPARTS

11

Schedulability Analysis

Multi-modal FP-schedulability requires high computation time.

No support for non-preemptive execution.

Schedulability Analysis

Set of real-timemodes

Yes: All deadlines are met.

No: There could be a deadline miss.

Pseudo-polynomial-time FP Schedulability Analysis

introductionmodelscontributionsconclusion

COPARTS

12

Problem Definition

Check FP-schedulability for any legal sequence of job arrivals and mode-change requests.

Given M1, M2, … … Mq, resources Ωij, transition duration δij, unchanged tasks τij, aborted tasks Aij :

Pseudo-polynomial-time FP Schedulability Analysis

introductionmodelscontributionsconclusion

COPARTS

13

Schedulability Conditions

t

Execution

Request Bound

Function (RBF)

Supply Bound

Function (SBF)

introductionmodelscontributionsconclusion

COPARTS

14

Schedulability Conditions

Mjijkt

Condition “SC5”

Condition “SC1”

Condition “SC2”

Condition “SC3”

Condition “SC4”

kt

COPARTS

15

FP Schedulability ConditionsPseudo-polynomial-time FP Schedulability Analysis

No existing MMS supports non-preemptible execution

Non-preemptive and iterative FB-Schedulability

Goal

1. Find Largest Busy-Intervals (Davis et al.2007) for any task

2. Response time considering multiple jobs in the busy-interal

3. Evaluate vulnerable jobs in all busy interval

tk

All Higher Priority Tasks

Supply from Periodic

Resource

Initial Condition

All Higher Priority Tasks

Response

Time

Blocking

Factor

introductionmodelscontributionsconclusion

COPARTS

16

FP Schedulability ConditionsPseudo-polynomial-time FP Schedulability Analysis

tk

introductionmodelscontributionsconclusion

ijkt

x

t

)( jd

a b

Carry-In

New mode tasks

Supply

COPARTS

17

Radar SimulationPerformance Evaluation

No loss of performan

ce Reclaimed cpu cycles could be used with low

criticality tasks.Figure Courtesy: Mathworks

FMCW Radar77 GHz

MATLAB Phased Array Toolbox

introductionmodelscontributionsconclusion

COPARTS

18

Simulation Settings

Environment MATLAB

Unchanged Tasks Tasks 5

Aborted Tasks Task 1

Resource

Tasks Set

)()()( ,10 iii

Compared against state-of-the-art algorithm by Phan et al. (2010).

introductionmodelscontributionsconclusion

Performance Evaluation

COPARTS

19

Simulation Results

Phan approach:Schedulability

using reachability (SURG)

Our approach: Schedulability using Bounded

Iteration

SUBI requires 2

sec to finish

introductionmodelscontributionsconclusion

Performance Evaluation

COPARTS

20

Conclusion

Problem: Existing MMS cannot exploit features of a

control system. Goal:

Develop a multi-mode systems for a shared platform

Non-preemptive executions with FP Contributions:

Designed a protocol, developed schedulability analysis, and determined parameters of a MMS.

introductionmodelscontributionsconclusion

COPARTS

21

Future Work

MMS Upon Multi-Core MMS Protocol to Exploit Multi-Core Schedulability Resource Allocation Thermal-Resilient Multicore Systems

Mixed Criticality Scheduling Exploit MMS Carry-In Concepts Exploit MMS Resource Allocation

introductionmodelscontributionsconclusion

COPARTS

22

Multi-Modal Systems

Questions?