Generators for Synthesis of QoS Adaptation in Distributed Real-time Embedded Systems

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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
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Roadmap

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

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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

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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

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MultiGraph Architecture
DSDE Domain-Specific Design Environments DSME
Domain-Specific Modeling Environments
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Generic Modeling Environment (GME)
Meta-modeling Environment
Domain-specific Modeling Environment
http//www.isis.vanderbilt.edu/Projects/gme/defaul
t.html
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Roadmap

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

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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.)
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QoS Adaptation A Control Systems Perspective
QuO Contract
SysConds
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MIC Approach
Adaptive QoS Modeling Language (AQML)
G
G
G
Symbolic Model Verifier (SMV) Model Checking
BBN Contract Definition Language
Matlab Simulink/Stateflow Simulation
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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

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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

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Adaptive Quality Modeling Language (AQML) (2)

• Computation Modeling
• Hierarchical dataflow representation
• Compounds, primitives
• Parameters for component instrumentation and
  customization

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Adaptive Quality Modeling Language (AQML) (3)

• Middleware Modeling
• Services and system condition objects
• Parameters for middleware instrumentation and
  customization

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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

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AQML Example ModelCase 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

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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

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Roadmap

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

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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

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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

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Generated M-script
create transitions t Stateflow.Transition
(chart) t.Source s4 t.Destination
s5 l sprintf('s\ns','timeInRegion gt
30/frameRate30','timeInRegion_w0')
t.LabelString l t.SourceOClock 9
t.DestinationOClock 9 t.MidPoint 151
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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,'Posit
ion',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 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'
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Matlab-Simulink Models
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Simulation Results
Desired frame rate
Actual frame rate

• 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

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Roadmap

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

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QuO Contract Generation

• AQME models translated to QuO contracts
  represented in Contract Definition Language (CDL)

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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

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Case Study CDL

• 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

• contract UAVsplitContract (
• callback UAVCallbacksSender_Control_Callbac
  k senderControl,
• syscond nowatch quoValueSC
  quo_scValueSCImpl currentRegion,
• syscond nowatch quoValueSC
  quo_scValueSCImpl negotiatedFrameRate,
• syscond nowatch quoValueSC
  quo_scValueSCImpl actualFrameRate,
• syscond quoValueSC
  quo_scValueSCImpl timeInRegion )
• state state_6 (
• (((long) timeInRegion ) gt 3) -gt state_5,
• (((long) actualFrameRate ) lt 27 and ((long)
  actualFrameRate ) gt 8) -gt state_3,
• (((long) actualFrameRate ) gt 27) -gt state_2
  )
• transition state_5 -gt state_6
• synchronous
• senderControl.setFrameRate( 10 )
• negotiatedFrameRate.longValue( 10 )

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Roadmap

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

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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

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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

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Extra Slides
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Conceptual structure
Domain Modeling
Development Platform
Mapping/Synthesis
Components
12/15/97
Integration Platform
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Typical System Development
Specs, Drawings, Diagrams, Equations etc.
Specifications
End-users Specify their systems
Synthesis
Simulation and/or analysis data
Applications
Programmers Implement Code
Domain-specific software apps
Configuration scripts
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The MultiGraph Solution
Specifications
End-users model their systems
Graphical Modeling Environment
Synthesis
Model Interpreter
Simulation and/or analysis data
Applications
Domain-specific software apps
Configuration scripts
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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

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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

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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