Karel de Grote-Hogeschool                  TERA-Labs                  www.kdg.be            Universiteit Antwerpen        ...
Introduction•  Problem Statement  – MDE has advantages  – Simulation is used often    •  For example: early deployment spa...
m of user static scheduling policy is defined here.current */ }     software. However, in the E.g. RMA version of SystemC ...
ExamplesS. Becker, H. Koziolek, and R. Reussner; The Palladio component model formodel-driven performance prediction, Jou...
ExamplesJ. Denil, H. Vangheluwe, P. Ramaekers, P. De Meulenaere, and S. Demeyer;DEVS for AUTOSAR platform modelling; in Pr...
Introduction•  Problem Statement  – MDE has advantages  – Simulation is used often  – PROBLEM: Calibration of simulation  ...
Calibration?•  Estimate model parameters to reflect   reality•  For example:  – Physical model: Gain of a motor  – Queuing...
Calibration?•  State of Art:  –  Instrument Source Code  –  Make test programs (trace driven)  –  Execute on Target or Cyc...
Motivating Examplewww.teralabs.org                         9http://Ansymo.ua.ac.be
windowPos                                 <                                                                               ...
Problem Revisited       SWCControl_Passenger                                SWC       SWC                               Lo...
Architecture•  Use target hardware for SW•  Use host for simulation                    Input Values and Triggers          ...
Generating Infrastructure  www.teralabs.org                            13  http://Ansymo.ua.ac.be
www.teralabs.org                         14http://Ansymo.ua.ac.be
Generating Infrastructure  www.teralabs.org                            15  http://Ansymo.ua.ac.be
www.teralabs.org                         16http://Ansymo.ua.ac.be
Generating Infrastructure  www.teralabs.org                            17  http://Ansymo.ua.ac.be
www.teralabs.org                         18http://Ansymo.ua.ac.be
Generating Infrastructure  www.teralabs.org                            19  http://Ansymo.ua.ac.be
Combining Models                                                          windowPos                                       ...
Generating Infrastructure  www.teralabs.org                            21  http://Ansymo.ua.ac.be
windowPos                                           <                                                                     ...
www.teralabs.org                         23http://Ansymo.ua.ac.be
Generating Infrastructure  www.teralabs.org                            24  http://Ansymo.ua.ac.be
Figure 7. The combined model using generic links to conn          invFriction                                             ...
Discussion•  Tooling: Combining different   formalisms?  – Super-meta-model•  More HW platforms, other   performance measu...
Conclusion•  Problem Statement  –  Calibration of simulation models•  Solution:  – Use MDE techniques (generative) to    c...
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Calibration of Deployment Simulation Models - A Multi-Paradigm Modelling Approach

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Presentation at the 2nd International Workshop on Model-driven Approaches for Simulation Engineering

(held within the SCS/IEEE Symposium on Theory of Modeling and Simulation part of SpringSim 2012)

Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details

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Calibration of Deployment Simulation Models - A Multi-Paradigm Modelling Approach

  1. 1. Karel de Grote-Hogeschool TERA-Labs www.kdg.be Universiteit Antwerpen ANSYMO www.ua.ac.beCalibration of Deployment Simulation ModelsA Multi-Paradigm Modelling Approach Joachim Denil Hans Vangheluwe Paul De Meulenaere Serge Demeyer
  2. 2. Introduction•  Problem Statement – MDE has advantages – Simulation is used often •  For example: early deployment space exploration www.teralabs.org 2 http://Ansymo.ua.ac.be
  3. 3. m of user static scheduling policy is defined here.current */ } software. However, in the E.g. RMA version of SystemC (2.0), 1600 processor. Due to the structure of the problem, dynamic ore_policy(vector<rt_task*> &tasks,sc_time %t) we propose in this this feature is still missing [3]. Therefore, preemptive scheduling does not lead to better results. So, since of user online scheduling policy is defined here. E.g. EDF */ } extension to work on paper the scheduling simulation capability this robot is not a highly safe-critical application, event driven SystemC models aiming to extend of its usage for real-timeis considered as the most feasible strategy in this Examples 8.scheduling assessment.Port basic approach of [7] is mapped to Re-scheduling by The Binding scheduling case. One comes up to this result from high-level analysis of SystemC by extending the scope considerably to this embedded information system. allow for aFlow to Experiment Results scheduling simulation into the complete integration of HW/SW ionembedded system co-design flow. proposed demonstrates the feasibility of the Table 1. Simulation Performance Results embedded systems design by means of an BCET ACET WCET ample. Simulation Framework Overview 3. As an example we exploit an autonomous Time triggered 540 ms 540 ms 540 msd with ultrasound distance sensors, lev camera, and sy s t e m a el Event Driven 331 ms 357 ms 431 msta link subsystem, where its entire specification S y st e m C Priority-based Ordering 335 ms 361 ms 435 ms m odel 17 tasks is captured os a task graph along with a n d Preemptive Scheduling e v e nts 335 ms 361 ms 435 ms HW Model properties (estimated max-min execution times). d a t a re ce pt io n 6. Conclusions and Future Workow starts with the allocated specification model.g in g a n d p re s e n t a t io n lo gm design,che d ule a s A dd -In u s e r s the functional specification is then s che du ling In this paper, a SystemC based scheduling simulator along lo g f ile s s im u lat ion e ng inento multiple processing elements (PEs). In this with its integrated environment is presented. It provides a envisaged generic hardware architecture for the G U I framework for assessing scheduling algorithms options, while s t a t ic o ff-lin e a lg o rit h m s e ctio n ocessing of this robot is a multi-processor system the bulk of the design is modeled in SystemC at a high d y n a m ic u t abstraction level. It is thus possible to exercise both hardwarem a set of Pes,lgi.e.,ma co-processornon e PCI FPGA csotmralaeif a o rit h o -lin a o g nd a microcontroller attached tose ctio n the mobile robot. and real-time software modules of system-level allowing early e rro r municate throughn a PCI bus (between PC and system performance assessment as well as verification and in je c t io a a set of wirelessf oRS232 modems (between rC ult analys is validation of different implementation alternatives and a lg o rit h m r es bot and PC). r in je c t io nto the inherently sequential e rro Due scheduling strategies. Application scenarios for modeling PE, tasks mappedProposed Simulationto be Figure 1. to the same PE need Framework distributed system is a challenging subject for future work in then scheduled statically orKlaus, andIn case Huss; Anto extend theSystemC framework for real- TheHastono, S. dynamically. S. integrates functional P. proposed simulation framework A. order integrated simulation methodology for global scheduling scheduling assessments is system on time implementation, scheduler on scheduling analysis. c validation with architectural aand scheduling explorationlevel; in Proceedings of IEEE Int. Real-re in the proposed framework isengine along with software code system level. The simulation a customizable Time Systems Symposium, 2004. 7. References scheduling simulator module. [1] C. M. Harmonosky, Simulation-Based Real-Time Scheduling:e process of generating SystemC models of the Review of Recent Developments, In Proc. of the 1995 Winter ormation processing of the robot is based on www.teralabs.org the Simulation Conference, December 1995. 3odel of the specification. This generated model http://Ansymo.ua.ac.be [2] SystemC, http://www.systemc.org.
  4. 4. ExamplesS. Becker, H. Koziolek, and R. Reussner; The Palladio component model formodel-driven performance prediction, Journal of Systems and Software, vol.82, no. 1, pp. 3-22, Jan. 2009. www.teralabs.org 4 http://Ansymo.ua.ac.be
  5. 5. ExamplesJ. Denil, H. Vangheluwe, P. Ramaekers, P. De Meulenaere, and S. Demeyer;DEVS for AUTOSAR platform modelling; in Proceedings of the 2011 SpringSimMulti-Conference: DEVS/TMS, 2011. www.teralabs.org 5 http://Ansymo.ua.ac.be
  6. 6. Introduction•  Problem Statement – MDE has advantages – Simulation is used often – PROBLEM: Calibration of simulation models•  Solution: – Use MDE techniques (generative) to calibrate models www.teralabs.org 6 http://Ansymo.ua.ac.be
  7. 7. Calibration?•  Estimate model parameters to reflect reality•  For example: – Physical model: Gain of a motor – Queuing system: Distribution of arrival times – In Previous examples: •  WCET •  Distribution of Execution Times www.teralabs.org 7 http://Ansymo.ua.ac.be
  8. 8. Calibration?•  State of Art: –  Instrument Source Code –  Make test programs (trace driven) –  Execute on Target or Cycle-true Simulator•  Cyber-Physical Systems: –  Input not only from environment but also from feedback! www.teralabs.org 8 http://Ansymo.ua.ac.be
  9. 9. Motivating Examplewww.teralabs.org 9http://Ansymo.ua.ac.be
  10. 10. windowPos < CInitAngularVelocity CInitPositionWindow 0.0 100.0 motorSignalMPM Design of+ the Power Window goingUp SWC windowPos CAtTop joinedUpDown AngularVelocity FAV Control_Passenger X + m 0.0 goingDown AtTop MotorGain < motorSignal AngularVelocity SWC 0.0 UP > SWC 50.0 Logic SWC + Multi-Paradigm Modelling (MPM): Control_Driver 1.0 friction > DC_Motor PsgrButtons AtBottom CAtBottom DOWN Cfriction X 0.0 10.0 UP “Model Everything invFriction windowPos DriverButtons TopOrBottom SWC ObjectIn at the right level(s) of abstraction, DOWN FeedBack Sensor_LoadmotorSignal + + = DrvChildLock ObjectDetected Controller using (an) appropriate formalism(s)” noObject 0.0 DrvIgnition ToMotor PsgrButton ForceDetect ObjectInWindow www.teralabs.org 10 http://Ansymo.ua.ac.be
  11. 11. Problem Revisited SWCControl_Passenger SWC SWC Logic SWCControl_Driver DC_Motor Deploy SWC Sensor_Load DrvDoor BodyLogic PsgDoor MPC5567 MPC5567 MPC5567 Performance Characteristics Body CAN www.teralabs.org 11 http://Ansymo.ua.ac.be
  12. 12. Architecture•  Use target hardware for SW•  Use host for simulation Input Values and Triggers Output Values and Traces Host Target Platform www.teralabs.org 12 http://Ansymo.ua.ac.be
  13. 13. Generating Infrastructure www.teralabs.org 13 http://Ansymo.ua.ac.be
  14. 14. www.teralabs.org 14http://Ansymo.ua.ac.be
  15. 15. Generating Infrastructure www.teralabs.org 15 http://Ansymo.ua.ac.be
  16. 16. www.teralabs.org 16http://Ansymo.ua.ac.be
  17. 17. Generating Infrastructure www.teralabs.org 17 http://Ansymo.ua.ac.be
  18. 18. www.teralabs.org 18http://Ansymo.ua.ac.be
  19. 19. Generating Infrastructure www.teralabs.org 19 http://Ansymo.ua.ac.be
  20. 20. Combining Models windowPos < CInitAngularVelocity CInitPositionWindow 0.0 100.0 motorSignal goingUp AtTop windowPos joinedUpDown AngularVelocity FAV X m 0.0 + + goingDown AtTop MotorGain < motorSignal AngularVelocity 0.0 > 50.0 + 1.0 > friction AtBottom AtBottom X Cfriction 0.0 10.0 invFriction windowPos TopOrBottom object FeedBackmotorSignal ObjectIn + = + ObjectDetected noObject 0.0 SWC PsgrButton Control_Passenger SWC SWC DrvButton Logic DC_Motor SWC DrvChildLock Control_Driver DrvIgnition SWC Sensor_Load www.teralabs.org 20 http://Ansymo.ua.ac.be
  21. 21. Generating Infrastructure www.teralabs.org 21 http://Ansymo.ua.ac.be
  22. 22. windowPos < CInitAngularVelocity CInitPositionWindow 0.0 100.0 motorSignal goingUp AtTop windowPos joinedUpDown AngularVelocity FAV X m 0.0 + + goingDown AtTop MotorGain < motorSignal AngularVelocity 0.0 > 50.0 + 1.0 > friction AtBottom AtBottom X Cfriction 0.0 10.0 invFriction windowPos TopOrBottom object FeedBackmotorSignal ObjectIn + = + ObjectDetected noObject 0.0 SWC PsgrButton Control_Passenger SWC SWC DrvButton Logic DC_Motor SWC DrvChildLock Control_Driver DrvIgnition SWC Sensor_Load Input Values and Triggers Output Values and Traces Host Target Platform www.teralabs.org 22 http://Ansymo.ua.ac.be
  23. 23. www.teralabs.org 23http://Ansymo.ua.ac.be
  24. 24. Generating Infrastructure www.teralabs.org 24 http://Ansymo.ua.ac.be
  25. 25. Figure 7. The combined model using generic links to conn invFriction TopOrBottom Results = + FeedBack our generated infrastructure match the values obtained by th noObject ObjectDetected hardware instrumentation. Execution Time (µs) Childlock Off ChildLock On SWC SWC Logic Control_Passenger SWC DC_Motor 20.375 12500 12000 19.875 2500 3000 SWC SWC Table 1. Results for the Control Driver runnable. SWC Logic Control_Driver DC_Motor Execution Time (µs) Childlock Off ChildLock On SWC Sensor_Load 11.375 9000 10000 10.875 6000 5000 ities in the different formalisms. Table 2. Results for the Control Passenger. Execution Time (µs) Childlock Off ChildLock On Execution Time (µs) Childlock Off ChildLock On 20.000 7500 4999 7.625 15000 15000 20.500 0 10001 Table 3. Results for the Sensor Load runnable. 20.875 7499 0 21.375 1 0 The obtained values from the different runnables can b DrvDoorTable 4. Results for the Logic runnable. The strange result MPC5567 used as input parameters for the system performance simula f the last row is because of a special condition thattion models. Validated using hardware measurements! only can ccur in the first execution round. Execution Time (µs) Childlock Off www.teralabs.org ChildLock On 6. DISCUSSION http://Ansymo.ua.ac.be On the tooling side of this approach a problem25 occu can 8.00 6000 3000
  26. 26. Discussion•  Tooling: Combining different formalisms? – Super-meta-model•  More HW platforms, other performance measure? –  Use other template•  Limitation: – Caching, pipelines, … www.teralabs.org 26 http://Ansymo.ua.ac.be
  27. 27. Conclusion•  Problem Statement –  Calibration of simulation models•  Solution: – Use MDE techniques (generative) to calibrate models www.teralabs.org 27 http://Ansymo.ua.ac.be

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