Presentation at the 18th International Conference on Engineering of Complex Computer Systems (ICECCS), 2013.07, Singapore, Singapore. More details about the paper at https://sites.google.com/site/vaneachiprianov/papers .

1. Model-driven performance prediction of
distributed real-time embedded defence systems
Katrina Falkner Nickolas Falkner James Hill Dan Fraser Marianne Rieckmann
Vanea Chiprianov Claudia Szabo Gavin Puddy Adrian Johnston Andrew Wallis

2. Agenda
• Model-driven engineering and System execution
modelling for defence systems
• The architecture of the performance prediction system
• Early validation on an Unmanned Air Vehicle (UAV)
• Conclusion and perspectives
University of Adelaide 2

3. Model-driven engineering and System
execution modelling for defence systems
• Requirements of DRE defence systems
– Long life-cycles
– Change in development philosophies
– Modular design
– Reuse
– Greater concern for non-functional
• Space, weight, power
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4. Model-driven engineering and System
execution modelling for defence systems
• Performance prediction
while(!perfModel.satistify(userPerfGoal)){
perfModel<-improvedPerfModel;
}
• Model-driven engineering
– Model
– Execute
• System execution modelling (SEM)
– Performance specificity
– Hardware testbeds
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5. The architecture of the performance
prediction system
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6. Modelling
• Modelling the System under study
(SUS) – the SEM
– Systemic structure
– Functional behaviour
– Workload
– Deployment
• Modelling Scenarios
– Simulate realistic interactions
– Analyse performance of SUS
– Scenario Domain Specific Language (DSL)
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7. Executing
• Executing the System execution model
(SEM)
– Application: SEM + scenarios
– Middleware: Data Distribution Service DDS
– Operating system
– Hardware
• Executing Scenarios
– Platform specific information
– Code generation of distributed units
– Deployment
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Defence needs

8. Evaluating and predicting
• Collect execution
traces
• Aggregate metrics
• Evaluate
if(perfModel.meet(
perfConstraints))
• Visualize
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9. Early validation on an Unmanned Air
Vehicle
• Scenario:
=> change in bandwidth
=> change in CPU workload
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UAV in the air
UAV going underwater

10. Early validation on an Unmanned Air
Vehicle
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Systemic structural
model of the SUS
Behavioural and
workload models
of the SUS

11. Early validation on an Unmanned Air
Vehicle
• Evaluating utilization:
u =
𝑠𝑒𝑟𝑣𝑖𝑐𝑒 𝑡𝑖𝑚𝑒
𝑟𝑢𝑛𝑡𝑖𝑚𝑒
uAIR=4.15%
uSUB=59.6%
for workload=150 msec
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Execution
traces of
the SEM

12. Conclusion and perspectives
• Model-driven performance prediction system
– Integration of realistic data sources
– Visualization of the causes of performance issues
– Understanding of models and relationships
• Perspectives
– Graphical Scenario DSL
– Performance DSL
– Multi-modelling DSL
University of Adelaide 12