1. Institute for Software Integrated Systems Vanderbilt University Generators for Synthesis of QoS Adaptation in Distributed Real- time Embedded Systems Sandeep Neema, Ted Bapty, Aniruddha Gokhale Institute for Software Integrated Systems, Vanderbilt University Jeff Gray University of Alabama, Birmingham http://www.isis.vanderbilt.edu/projects/pces GPCE-02, Pittsburgh, PA, October 6-8, 2002

2. Roadmap Model Integrated Computing (MIC) Motivation Adaptive QoS Modeling Language (AQML) Simulation of QoS adaptation Synthesis of QoS adaptation Conclusions/Future Work

3. Model Integrated Computing (MIC) Resulting from 15 years of research on computer-based embedded systems in aerospace, instrumentation, manufacturing and robotics. Common challenges: Software and environment are inseparable Need for adaptability to changing environment and end-user needs Complex, heterogeneous applications Stringent reliability and dependability requirements

4. Model Integrated Computing (MIC) Represent the information that directly or indirectly determines the structure of computations using domain-specific modeling paradigms Capture the relationship between domain models, analysis models and executable models Validate, analyze the domain models using generic tools Synthesize computations from the domain models

5. DSME Model Interpreters Models DSDE Model Interpretation Application Domain App. 1 App. 2 App. 3 Meta- Level Translatio n Meta-modeling Interface Formal Specifications Application Evolution Environment Evolution MultiGraph Architecture DSDE: Domain-Specific Design Environments DSME: Domain-Specific Modeling Environments

6. Generic Modeling Environment (GME) http://www.isis.vanderbilt.edu/Projects/gme/default.html Meta-modeling Environment Domain-specific Modeling Environment

7. Roadmap Model Integrated Computing (MIC) Motivation Adaptive QoS Modeling Language (AQML) Simulation of QoS adaptation Synthesis of QoS adaptation Conclusions/Future Work

8. Motivation RT Middleware solutions enable highly-complex Distributed Real-time Embedded (DRE) systems QoS adaptive frameworks (QuO * ) enable abstraction of QoS parameters and QoS adaptation Low-level of abstraction for specification of adaptation –Difficult to validate/verify System behavior not apparent in specification of adaptation –QoS adaptation software is equivalent to a discrete controller for a highly non-linear system –Could result in instability –Need to simulate/verify Need: Specifications of behavior separated from software concerns * QuO: Quality Objects (BBN Tech.)

9. QoS Adaptation: A Control Systems Perspective QuO Contract Application Middleware OS-s Hardware+Network A A A A S S S S SysConds desired QoS error observed QoS + – Plant Feedback Controller

10. MIC Approach Adaptive QoS Modeling Language (AQML) Matlab Simulink/Stateflow Simulation Symbolic Model Verifier (SMV) Model Checking BBN Contract Definition Language End-users model their systems in AQML 1 2 Simulations are generated from models GGG G = Generator/Model Interpreter 3 Inputs for model checkers is generated 4 CDL output is generated when simulation and model checking results are satisfactory 5 QoS adaptive DRE system is deployed

11. Roadmap Model Integrated Computing (MIC) Motivation Adaptive QoS Modeling Language (AQML) –Adaptive Behavior, Computation, Middleware Simulation of QoS adaptation Synthesis of QoS adaptation Conclusions/Future Work

12. Adaptive Quality Modeling Language (AQML) QoS Adaptation Modeling Hierarchical, parallel, finite state machine representation States capture system-wide QoS configurations Transitions represent cause and effect of change in operating conditions Data/Event variables represent the interface to the operating environment

13. Adaptive Quality Modeling Language (AQML) (2) Computation Modeling Hierarchical dataflow representation –Compounds, primitives Parameters for component instrumentation and customization

14. Adaptive Quality Modeling Language (AQML) (3) Middleware Modeling Services and system condition objects Parameters for middleware instrumentation and customization

15. Adaptive Quality Modeling Language (AQML) (4) Interaction Modeling Interaction of controller (adaptation state-machine) with sensors/actuators (parameters in middleware/computation modeling), captured with Control connection References for cross-hierarchy connection

16. AQML Example Model Case Study Dataflow model of the UAV video- streaming application Sender, Distributor, and Receiver, distributed application components ActualFrameRate, TimeInRegion, … middleware syscond (parameter) objects ResvWithDropping reference the adaptation behavior

17. AQML Example Model (2) Case Study NormalLoad, HighLoad, and ExcessLoad states within the QoS parameter space actualFrameRate, frameRate, … data variables within state machine interface with the syscond objects Duty, Test sub-states of HighLoad state Transition from duty to test enabled when time in duty exceeds 30 ticks frameRate variable set to 30 when transitioning from duty to test state

18. Roadmap Model Integrated Computing (MIC) Motivation Adaptive QoS Modeling Language (AQML) Simulation of QoS adaptation Synthesis of QoS adaptation Conclusions/Future Work

19. Simulation of QoS adaptation Matlab® Simulink®/Stateflow® used as the simulation engine Model interpreters generate Matlab M-code to construct Simulink/Stateflow representation User provides a network simulation (plant) model, that can simulate various load conditions in the network AQML Model Matlab M-file G Plant model Simulation results Controller model

20. Simulation Generator Stateflow represents hierarchy through graphical containment on a single diagram bounding rectangle of a sub-state is enclosed by the bounding rectangle of a super-state Phase 1: traverses the AQML model-object hierarchy to compute the bounding rectangles for states Phase 2: traverses the AQML model-object hierarchy, and emits Matlab M-script code to construct Stateflow objects using the Matlab- Stateflow API

21. Generated M-script % Matlab script for creation of a Stateflow Model % generated: Friday, October 04, 2002 sfnew('UAV'); root = sfroot; mach = root.find('-isa', 'Stateflow.Machine'); chart = mach.find('-isa', 'Stateflow.Chart'); chart.Name = 'Controller'; set_param(gcbh,'Position',[120 40 300 160]); % create states s0 = Stateflow.State(chart); s0.Name = 'ResvWithDropping'; s0.LabelString = 'ResvWithDropping'; s0.Decomposition = 'EXCLUSIVE_OR'; s0.Position = [0 0 490 440]; s1 = Stateflow.State(chart); s1.Name = 'NoReservation'; s1.LabelString = 'NoReservation'; s1.Decomposition = 'EXCLUSIVE_OR'; s1.Position = [20 40 450 280]; % create transitions t = Stateflow.Transition(chart); t.Source = s4; t.Destination = s5; l = sprintf('%s\n%s','[timeInRegion >= 30]/frameRate=30','timeInRegion_w=0'); t.LabelString = l; t.SourceOClock = 9; t.DestinationOClock = 9; t.MidPoint = [151 190]; … % create data d = Stateflow.Data(chart); d.Name = 'frameRate'; d.DataType = 'int32'; d.Scope = 'OUTPUT_DATA'; d.Name = 'actualFrameRate'; d.DataType = 'int32'; d.Scope = 'INPUT_DATA'; …

22. Matlab-Simulink Models

23. Simulation Results Results depicts variation in the desired frame rate and the actual frame rate observed on the simulated network Results are obtained against a simulated sinusoidal loading profile on the network Results can be used to fine-tune the adaptation policy against different loading profiles (bursty, sharp transients, gradual persistent, …) Benefit Rapid prototyping and visualization of the responsiveness and behavior of the QoS adaptation policy Desired frame rateActual frame rate

24. Roadmap Model Integrated Computing (MIC) Motivation Adaptive QoS Modeling Language (AQML) Simulation of QoS adaptation Synthesis of QoS adaptation Conclusions/Future Work

25. QuO Contract Generation AQME models translated to QuO contracts represented in Contract Definition Language (CDL) UAV Model G QuO Contract (CDL) BBN QuoGen.CPP.H Target Appl./MW

26. Quo Contract (CDL) Generator CDL Contracts are expressed in flat finite-state machine like representation –Guards, and actions in transitions are method calls over middleware objects Phase 1: translates the hierarchical parallel FSM representation to a flat FSM representation Phase 2: traverses the flattened FSM object-graph and emits CDL code –parse guards, triggers, and action expressions, –traverse Abstract Syntax Tree, and emit code –for any reference to a data or event variable in the expression, emit the get/set method call on the middleware object

27. Case Study: CDL contract UAVsplitContract ( callback UAVCallbacks::Sender_Control_Callback senderControl, syscond nowatchquo::ValueSC quo_sc::ValueSCImpl currentRegion, syscond nowatchquo::ValueSC quo_sc::ValueSCImpl negotiatedFrameRate, syscond nowatchquo::ValueSC quo_sc::ValueSCImpl actualFrameRate, syscond quo::ValueSC quo_sc::ValueSCImpl timeInRegion ) { state state_6 ( (((long) timeInRegion ) >= 3) -> state_5, (((long) actualFrameRate ) = 8) -> state_3, (((long) actualFrameRate ) >= 27) -> state_2 ) { } … transition state_5 -> state_6 { synchronous { senderControl.setFrameRate( 10 ); negotiatedFrameRate.longValue( 10 ); timeInRegion.longValue( 0 ); } … }; Complex CDL contract synthesized from models CDL output is post- processed by the BBN QuoGen compiler to generate code for the BBN QuO adaptation framework Generated Adaptation code monitors the QoS state of the distributed real-time system, and execute transitions and actions to bring the system in a desired operational state

28. Roadmap Model Integrated Computing (MIC) Motivation Adaptive QoS Modeling Language (AQML) Simulation of QoS adaptation Synthesis of QoS adaptation Conclusions/Future Work

29. Conclusions MIC enables better design and synthesis of highly complex QoS adaptation contracts by providing a domain-specific, higher level of abstraction The approach shortens the design, implement, test, and iterate cycle by providing early simulation, and analysis capabilities The approach facilitates change maintenance and evolution as minimal changes in the models effect large changes in the low-level (textual, code-base) CDL specification

30. Future Work Investigating results from control-theory for synthesizing adaptation behaviors Extending the QoS adaptation modeling from a single centralized adaptation strategy, to several distributed local adaptation strategies, and their coordination and synchronization Enhancement of the middleware and dataflow modeling aspects of the AQME

31. Extra Slides

32. Conceptual structure 12/15/97 Components Domain Modeling Mapping/Synthesis Integration Platform Development Platform

33. Typical System Development Simulation and/or analysis data Synthesis Domain-specific software apps Configuration scripts End-users Specify their systems Applications Specs, Drawings, Diagrams, Equations etc. Programmers Implement Code Specifications

34. The MultiGraph Solution Simulation and/or analysis data Domain-specific software apps Configuration scripts Application Evolution Graphical Modeling Environment End-users model their systems Model Interpreter Applications Specifications Synthesis

35. MIC Approach Creation of a Domain-Specific Modeling Language –QoS Adaptive Behavior –Middleware Interfaces –Application QoS parameters Generation of QoS adaptation code –Contract Definition Language (CDL) output Generation of Simulations –Verify behavior over points in the envelope of operation, using same models Analysis of system –Model Checking over full operational envelope, using same models

36. Adaptive QoS Modeling Environment (AQME) QoS Specification and Adaptation modeling –Hierarchical, parallel, finite state machine representation –States capture system-wide QoS configurations –Transitions represent cause and effect of change in operating conditions –Data/Event variables represent the interface to the operating environment

37. Adaptive QoS Modeling Environment (AQME) Software modeling –Hierarchical dataflow representation Compounds, primitives –Parameters for component instrumentation and customization Middleware modeling –Services and system condition objects –Parameters for middleware instrumentation and customization