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

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

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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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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The architecture of the performance prediction system

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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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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

Evaluating and predicting

• Collect execution traces

• Aggregate metrics

• Evaluate if(perfModel.meet(

perfConstraints))

• Visualize

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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

Early validation on an Unmanned Air Vehicle

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Systemic structural model of the SUS

Behavioural and workload models of the SUS

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

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