Minimizing CPU Shortage Risks in Integrated Embedded Software

Minimizing CPU Time Shortage Risks in Integrated Embedded Software!

Shiva Nejati, Morayo Adedjouma, Lionel C. Briand!SnT Centre, University of Luxembourg!

!Jonathan Hellebaut, Julien Begey, and Yves Clement!

Delphi Automotive, Luxembourg!!

November 14, 2013!

Today’s cars are developed in a distributed way

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Software integration is essential in distributed development

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Software integration involves multiple stakeholders

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•  Develop software optimized for their specific hardware

•  Provide part suppliers with

runnables (exe)

•  Integrate car makers software with their own platform

•  Deploy final software on ECUs and send them to car makers

Car Makers Part Suppliers

Stakeholders have different objectives

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•  Objective: Effective execution and synchronization of runnables

•  Some runnables should execute simultaneously or in a certain order

•  Objective: Effective usage of CPU time

•  The CPU time used by all

the runnables should remain as low as possible over time

Car Makers Part Suppliers

0ms 5ms 10ms 15ms 20ms 25ms 30ms

0ms 5ms 10ms 15ms 20ms 25ms 30ms

0ms 5ms 10ms 15ms 20ms 25ms 30ms

It is challenging to satisfy all objectives

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4ms

3ms

2ms

✔

✗

Car Makers Part Suppliers r0 r1 r2 r3

Execute r0 to r2 together Minimize CPU time usage

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Our approach to minimization of CPU time usage

Optimization

while satisfying synchronization/temporal constraints

Explicit Time

Model for real-time embedded systems

Search

meta-heuristic single objective search algorithms

10^27

an industrial case study with a large search space

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Problem Formulation

0ms 5ms 10ms 15ms 20ms 25ms 30ms

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Scheduling mechanism in automotive systems

AUTOSAR scheduling also known as static cyclic scheduling

period = 5ms period = 10ms

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How to minimize CPU time usage?

4ms

3ms

By setting offsets (i.e., initial delays) for runnables

0ms 5ms 10ms 15ms 20ms 25ms 30ms

0ms 5ms 10ms 15ms 20ms 25ms 30ms

o0=0, o1=0, o2=0, o3=0

o0=0, o1=5, o2=5, o3=0

r0 r1 r2 r3

0ms 5ms 10ms 15ms 20ms 25ms 30ms

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How to satisfy synchronization constraints?

Runnables r0, r1, and r2 should run in the same time slot infinitely often

r0 r1 r2 r3o0=0, o1=5, o2=5, o3=0

oi ⌘ oj mod(gcd(periodi, periodj))for ri, rj 2 {r0, r1, r2}

0ms 5ms 10ms 15ms 20ms 25ms 30ms

o0=0, o1=5, o2=10, o3=0 ✗

Classical number theory (Chinese remainder theorem)

Runnables r0, r1, and r2 run in the same time slot infinitely often iff

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Solution Overview

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Single-objective Search algorithms hill Climbing and tabu Search and their variations

Solution Representation

a vector of offset values: o0=0, o1=5, o2=5, o3=0 Tweak operator

o0=0, o1=5, o2=5, o3=0 à o0=0, o1=5, o2=10, o3=0

Synchronization Constraints offset values are modified to satisfy constraints

Fitness Function max CPU time usage per time slot

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Case Study and Experiments

An automotive software project with 430 runnables

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5.34ms 5.34ms5 ms

Time

CPU

tim

e us

age

(ms)

CPU time usage exceeds the size of the slot (5ms)

Without optimization

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CPU time usage always remains less than 2.13ms, so more than half of each slot is guaranteed to be free

2.13ms

5 ms

Time

CPU

tim

e us

age

(ms)

After applying our work

(ms)

(s)

Best CPU usage

Time to find Best CPU usage

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Experiment Results (Sanity Check) Our algorithms were better than random search

Tabu C

PU ti

me

usag

e (m

s)

Only Tabu search was not better than random search

Random

Hill Climbing

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Experiment Results (Effectiveness) Hill Climbing performed best compared to other algorithms Running Hill Climbing for three times, it hits the best result with a probability of 87.5% Our results were better than some existing (deterministic) algorithms based on real-time scheduling theory

•  Feasibility analysis

–  Schedulable or not? v  We perform optimization by finding the best solutions among all

the feasible ones

•  Logical models –  Correct/incorrect?

v  We provide an explicit time model enabling us to perform a quantitative analysis

•  Symbolically represent the model and rely on model-checkers or

constraint solvers –  State explosion problem

v  We scale to very large search spaces: 10^27

Related Work

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Summary

Optimization

while satisfying synchronization/temporal constraints

Explicit Time

Model for real-time embedded systems

Search

meta-heuristic single objective search algorithms

10^27

an industrial case study with a large search space