Predictable Timing of Cyber-Physical Systems Future Research Challenges

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1 Predictable Timing of Cyber- Systems Future Research Challenges DREAMS Seminar, EECS, UC Berkeley January 17, 2012 David Broman EECS Department UC Berkeley, USA Department of Computer and Information Science Linköping University, Sweden 2 Agenda Part I Semantic gap regarding time Part II Bridging the gap the PRETIL project Semantic gap Reducing the gap Part III Utilizing a bridged gap virtual optimization of CPS Part III Utilizing a bridged gap

2 3 Part I Semantic gap regarding time Semantic gap Reducing the gap Part III Utilizing a bridged gap 4 Modeling Cyber- Systems Model Equation-based model Abstraction physical modeling Networking s System Actuators system (the plant) Semantic gap Reducing the gap Embedded systems (computation) Part III Utilizing a bridged gap

3 Equation-Based Object-Oriented (EOO) Languages 5 Domain-Specific Language (DSL) Primarily domain: Modeling of physical systems Multiple physical domains: e.g., mechanical, electrical, hydraulic Equation-Based Object-Oriented (EOO) Models and Objects Object in e.g., Java, C++: object = data + methods Objects in EOO languages: object = data + equations Equation-Based Object-Oriented (EOO) Languages 6 Domain-Specific Language (DSL) Primarily domain: Modeling of physical systems Multiple physical domains: e.g., mechanical, electrical, hydraulic connections! ports! Equation-Based Object-Oriented (EOO) objects (components)! Models and Objects Object in e.g., Java, C++: object = data + methods Objects in EOO languages: object = data + equations EOO model (textual) EOO model (graphical)

4 Equation-Based Object-Oriented (EOO) Languages 7 Domain-Specific Language (DSL) Primarily domain: Modeling of physical systems Multiple physical domains: e.g., mechanical, electrical, hydraulic Equation-Based Object-Oriented (EOO) Models and Objects Object in e.g., Java, C++: object = data + methods Objects in EOO languages: object = data + equations Acausality At the equation-level u = R * i At the object connection level Equation-Based Object-Oriented (EOO) Languages 8 Domain-Specific Language (DSL) Primarily domain: Modeling of physical systems Multiple physical domains: e.g., mechanical, electrical, hydraulic Direction not determined at modeling time! Equation-Based acausal (non-causal) Object-Oriented (EOO) Models and Objects Object in e.g., Java, C++: object = data + methods Variables! Objects in EOO languages:! Potential! object = data + equations! Flow! Acausality causal At the equation-level topology! u = R is * lost! i At the object connection level

5 Equation-Based Object-Oriented (EOO) Languages 9 Domain-Specific Language (DSL) Primarily domain: Modeling of physical systems Multiple physical domains: e.g., mechanical, electrical, hydraulic Equation-Based acausal (non-causal) Object-Oriented (EOO) Models and Objects Object in e.g., Java, C++: object = data + methods Objects in EOO languages: object = data + equations Acausality causal At the equation-level u = R * i At the object connection level Equation-Based Object-Oriented (EOO) Languages 10 Domain-Specific Language (DSL) Primarily domain: Modeling of physical systems Multiple physical domains: e.g., mechanical, electrical, hydraulic Equation-Based Object-Oriented (EOO) Models and Objects Object in e.g., Java, C++: object = data + methods Objects in EOO languages: object = data + equations Modelica VHDL-AMS gproms MKL (SPICE) Acausality At the equation-level u = R * i At the object connection level

6 Modeling Cyber- Systems 11 Platform 1 Actuator Plant 2 Computation 1 Network Platform 2 Delay 1 Computation 4 Platform 3 Delay 2 Computation 2 Model Equation-based model Abstraction physical modeling Computation 3 Plant 12 Actuator Different models of computation C-code System s Networking Actuators system (the plant) Embedded systems (computation) Modeling the Systems for Computing and Networking Ptolemy II Heterogenous modeling environment supporting many different models of computation (MoC). For example, synchronous dataflow (SDF), discrete-event (DE), process networks (PN), etc. PTIDES Currently implementation in Ptolemy. Modeling of event-based realtime distributed systems. Based on DE semantics. Synchronous reactive languages For example, Lustre, Signal and Esterel Next versions of Modelica New semantics for synchronouse discrete semantics (for improved code generation). Simulink And all other languages/environments not listed here! 12

7 Simulation the CPS 13 Platform 1 Actuator Plant 2 Network Platform 2 FMI Computation 1 Computation 4 Delay 1 Computation 2 Platform 3 Delay 2 Model Equation-based model Computation 3 Plant 12 Actuator Different models of computation C-code System s Networking Actuators system (the plant) Embedded systems (computation) Simulation the CPS 14 Platform 1 Actuator Plant 2 Computation 1 Network Platform 2 Delay 1 Model Equation-based model Computation 4 Computation 2 Platform 3 Delay 2 Software-in-the-loop Computation 3 Plant 12 Actuator (SIL) simulation Different models of computation Hardware-in-the-loop (HIL) simulation Code generation C-code system available? System s Networking Actuators system (the plant) Embedded systems (computation)

8 Simulation the CPS 15 Platform 1 Actuator Plant 2 Network Platform 2 Model Computation 4 Computation 2 Predictable Timing of Cyber- Systems Platform 3 Delay 2 Software-in-the-loop Computation 3 Plant 12 Actuator meaning that the continuous-time (SIL) simulation timing behavior for Different models of computation Equation-based model SIL simulation HIL simulation Real-time system execution Code Hardware-in-the-loop Note that predictability is a continuum. generation (HIL) simulation The cyber can be made deterministic, but the physics cannot. C-code Computation 1 Delay 1 system available? System s Networking Actuators system (the plant) Embedded systems (computation) Model and Timing Problems 16 Cyber timing problems Model problems Incorrect System of Equations Validation of models Incorrect Parameters Control Delay From sampling to actuation Jitter Variation of start times (e.g., clock accuracy, architecture) Transient Errors E.g., loss data packets. Related to robustness. (Wittenmark et al., 1996) Communication Computation Clock sync, IEEE 1588 Hard to predict. Large model libraries. Mature tools. Bounded delays Precision-timed machines Modelica / MKL Precision-timed - Scratchpad memory - Timing instructions - Thread-interleaved piplines WCET of tasks Ptolemy II / PTIDES Semantic gap regarding time PRET Timingconstraints of tasks Automatic allocation of scratchpads? How to ensure that compilation is semantically correct regarding time?

9 17 Part II Bridging the gap the PRETIL project Precision-Timed Intermediate Language (PRETIL) High-level requirements 18 Modelica / MKL Ptolemy II / PTIDES Other MoC and tools Make code generation from source language to PRETIL simple (e.g., via suitable API) Support multiple modeling (source) languages Expose language constructs for (physical) execution time PRETIL Hide (abstract away) architecture dependent details (e.g., scratchpad) PRETIL compiler Formal semantics reason about correctness of execution time PRET PRET PRET General purpose CPU PRET PRET Enable comparison of platforms

10 Execution time a correctness factor 19 Worst-case execution time (WCET) Best-case execution time (BCET) Estimated upper bound of WCET Challenge to make it tight Sketch - primitives for handling time (pseudo-code, part of research to be performed) 20 F(x 1,,x n ) is a function in the language with n parameters.! Static Usage of execution time Propagating WCET info up the tool chain: - For meta-programming (static scheduling) - For tool support (e.g., show WCET for specific actors in Ptolemy) Propagate time constraint downwards constraint WCET(f) < 10ms! Execute with padding (exact time) execute f(3,2) during 10ms! Dynamic usage of execution time Execute with padding without guarantees execute f(3,2) during 10ms else! Use WCET/BCET info dynamically in the model/program. if WCET(f) > 10ms then else! WCET of parameterized functions in runtime using parametric WCET analysis (Lisper, 2003) if WCET(f(x 3 = v)) > 10ms then else!

11 Proposed Infrastructure Overview 21 Part I: Modeling language front end Part II: PRETIL front end Part III: PRETIL backend Part IV: Runtime environment Part I Modeling language front end 22 Research challenge 1: To design (or extend) an intermediate language that hides architecture details and exposes language constructs for programming with (physical) execution time.

12 Proposed Infrastructure Overview 23 Part I: Modeling language front end Part II: PRETIL front end Part III: PRETIL backend Part IV: Runtime environment Part II PRETIL Front end 24 Research challenge 2: To statically guarantee that timing constraints defined for high-level models hold during run-time. Formally verified compilers (Leroy, 2009) Translation Validation Infrastructure (Necula, 2000)

13 Proposed Infrastructure Overview 25 Part I: Modeling language front end Part II: PRETIL front end Part III: PRETIL backend Part IV: Runtime environment Part III PRETIL Back end 26 Research challenge 3: To optimize allocation of bounded memory resources so that both memory constraints and timing constraints hold simultaneously.

14 Proposed Infrastructure Overview 27 Part I: Modeling language front end Part II: PRETIL front end Part III: PRETIL backend Part IV: Runtime environment Part IV Runtime environment 28 Research challenge 4: To guarantee safe execution concerning timing of a deployed binary of machine code, without trusting the correctness of the compiler, e.g., by executing a lightweight safety proof before executing the binary. Proof-carrying code (Necula, 1997)

15 29 Part III Utilizing a bridged gap virtual optimization of CPS 30 Simulation with Predictable Timing Platform 1 Actuator Plant 2 Computation 1 Network Platform 2 Delay 1 Model Equation-based model Computation 4 Computation 2 Platform 3 Delay 2 Software-in-the-loop Computation 3 Plant 12 Actuator (SIL) simulation Different models of computation The PRETIL project aims at adding one piece of the puzzle to getting predictable timing of CPS System s Networking Actuators system (the plant) Embedded systems (computation)

16 Optimization with Predictable Timing 31 Platform 1 Actuator Plant 2 Computation 1 Network Platform 2 Delay 1 Equation-based model Computation 4 Computation 2 Platform 3 Delay 2 Computation 3 Plant 12 Actuator Software-in-the-loop (SIL) simulation Different models of computation Design optimization problems Parameter optimization of physical objects (e.g., thickness of shafts) Architecture parameters, e.g., minimize clock frequency to lower energy consumptions. Predictable timing with correct timing constraints are essential to performing the optimization on a global CPS model Hard problems. One approach is to combine CPS simulation with local search heuristics (e.g., tabu search or simulated annealing). Conclusions and Summary 32 New project in the Ptolemy group (starting Jan 2012). Modelica / MKL Ptolemy II / PTIDES Overall challenge To establish a new formal foundation of timing predictability for the semantics of correct translation/ compilation from high-level CPS modeling languages down to machine code for PRET machines. Semantic gap regarding PRETIL time PRETIL compiler Thank you for listening! PRET

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