Model-driven performance prediction of distributed real-time embedded defence systems
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Transcript of Model-driven performance prediction of distributed real-time embedded defence systems
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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
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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
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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
University of Adelaide 3
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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
University of Adelaide 4
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The architecture of the performance prediction system
University of Adelaide 5
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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)
University of Adelaide 6
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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
University of Adelaide 7
Defence needs
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Evaluating and predicting
• Collect execution traces
• Aggregate metrics
• Evaluate if(perfModel.meet(
perfConstraints))
• Visualize
University of Adelaide 8
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Early validation on an Unmanned Air Vehicle • Scenario:
=> change in bandwidth
=> change in CPU workload
University of Adelaide 9
UAV in the air
UAV going underwater
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Early validation on an Unmanned Air Vehicle
University of Adelaide 10
Systemic structural model of the SUS
Behavioural and workload models of the SUS
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Early validation on an Unmanned Air Vehicle
• Evaluating utilization:
u =𝑠𝑒𝑟𝑣𝑖𝑐𝑒 𝑡𝑖𝑚𝑒
𝑟𝑢𝑛𝑡𝑖𝑚𝑒
uAIR=4.15%
uSUB=59.6%
for workload=150 msec
University of Adelaide 11
Execution traces of the SEM
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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