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Q uantitative E valuation of E mbedded S ystems. Mutual introductions The context of the course: Model Based / Driven Design Organisation of the course. Introducing the lecturers. Marco Zuniga (TUD). Pieter Cuijpers (TU/e). Anne Remke (UT). Marielle Stoelinga (UT).
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Quantitative Evaluation of Embedded Systems • Mutual introductions • The context of the course: Model Based / Driven Design • Organisation of the course
Introducing the lecturers Marco Zuniga (TUD) Pieter Cuijpers (TU/e) Anne Remke (UT) MarielleStoelinga (UT)
Why a tele-lecture ? • Link between education and research • 3TU cooperation :Specialization in research vs Broad engineering education • Efficiency
Why a class-room ? flipped • More time for questions & (tele)-communication • Rewind button • Better insight in your progress • More convenient homework
Last years evaluation (warning) • Bad tele-connections • Three (too) different topics • Too many notational conventions • Too abstract for hands-onembedded systems enthousiasts • Too much mandatory homework
bandwidth energy timing battery drain up-time overflow chance of failure Model-based Design worst-case average-case package loss latency memory cost deadline miss throughput measurements best-case time-outs robustness
The Engineering Design Cycle Specification Design Implementation Deployment & Maintenance THE COST OF FIXING SOFTWARE BUGS (BOEHM)
Model Based Design Specification Design Implementation Model Checking Deployment & Maintenance
Model Driven Design Specification Design Implementation State space exploration Programming paradigms Code Generation Deployment & Maintenance
Next Generation Computing Quality = Quantity • Deadlines • Power usage • Fault tolerance • Performance Trends: • Complex • Highly networked • Failures = fact of life Needed: • Systematic Quant. Analysis at Design-time • Multi-disc. approach • QEES!
State based Petri-nets Probabilistic Parameterized Timed Data Discrete Max-plus algebra Differential equations Continuous Event based Automata Dynamic Behavior convex Model Checking CTL* monotone Quantitative (Numerical) Properties Qualitative (Logical) pCTL linear LTL tCTL modal µ-calculus
Contents of the course • 3 Typical quantitative formalisms: Dataflow, Timed Automata, Markov Chains • 1 Quantitative analysis method for Dataflow • 3 Model-checking methods for TA and MC • 3 Tools: SDF3, UPPAAL, PRISM • 1 Case study
Case: Cyber Physical Systems Computation Communication network Cyber Physical Control Sensing Acting Physical World
Case: Cyber Physical Systems Determine an appropriate communication schedule that guarantees given latency and throughput constraints for this control network and predict the associated network load. Sensor 1 Temperature Actor 1 Valve Comp. Inner control Sensor 2 Pressure Actor 2 Motor xyz Comp. Emergency detection Sensor 3 Camera Actor 3 Motor rot. Comp. Image processing Sensor 4 Microphone Physical World
General planning of QEES • Dataflow - Timed Automata - Probabilistic Automata • Tele-lectures & flipped classroom • Watch videos at home… …make exercises in class • Some additional material in class • One mandatory assignment (pass/fail)(One case-study document – to be updated 3 times) • One exam