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
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Minimizing CPU Shortage Risks in Integrated Embedded Software
ASE 2013 presentation - Research project with Delphi on embedded software integration and minimizing risk of CPU shortage
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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
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