1. Presented By: Darlene Banta
Model-Based Computing for Design and Control of Reconfigurable Systems Markus Fromherz, Daniel Bobrow, Johan de Kleer
Presented By: Darlene Banta

2. What is a Reconfigurable System?
Complex electro-mechanical products (high end printers and photocopiers)
Designed as families with reusable modules put together in different manufacturable configurations
Modules controlled locally by software that must take into account the entire configuration

3. The Problem
How to Build a system control framework for systems that can be configured out of different modules
To decide at design time whether a proposed module is a worthwhile addition to the system

4. The Solution: Qualitative Constraint Based Models
Primary Task is CONTROL
Approach to the design, control and evaluation of complex systems
Software engineers become model builders
Planning and constraint satisfaction replace program execution
The control software in the machine explicitly constructs a plan and monitors its execution in real time
Hierarchical Control Software Architecture to mirror the architecture of the machine itself

5. Solution Goals Increase productivity of software developers
Improved communication among different subsystem engineers (mechanics, electronics, software, etc)
Ensure consistency across different engineering tasks (design, control, testing, diagnosis)
Enable automatic modular configuration of the resulting systems of the controller

6. System Controller
Breaks system’s functions down into module functions and coordinates the modules to produce the desired documents
Determine operations that will complete the task successfully
Optimize machine productivity
Generate and commit to schedules incrementally

7. 1. Modeling Set of connected components, each contains:
Structural Description
Behavioral Description
Declarative modeling approach to use models for other tasks
Need to provide the necessary information to enable the control task
Planning – identify component capabilities in sequence
Scheduling – feasible timings for capabilities in a plan
Derives constraints from the physical structures of devices

8. (Module Modeling) Specify module components and their connections
Define itineraries – the mappings from module commands to component commands
Integrates component control to high-level commands
Control Module receives a command and sends required component commands to its components

9. 2. Planning
Forward simulation with discrete events and event propagation
Creates a sequence of modules that should be visited in order
Possible to cache the results for various plans, thus creating an efficient part of the process

10. 3. Scheduling
Solves time constraints in order to find a feasible schedule while maximizing productivity
Generate a schedule incrementally
Scheduling Algorithms and Architectures
Amount of look-ahead
Timing of when to commit to parts of the incremental schedule

11. 4. Modeling Language Component Description Language (CDL)
Declarative specification of input/output constraints
Provides
Behavioral statements
Specification of structural elements
Based on C++ Syntax
Translate language into concurrent constraint programming (CCP)
Simulation
Partial Evaluation
Abduction
General Reasoning

12. How It Works Theoretically
Machine adjusts to its own machine model
Modules pass up their module models to the system controller
At the system controller module models composed to a machine model
At run time, given the machine model and specifications, the system controller plans the module operations that need to be executed then schedules these operations

13. Configuration Analysis
Individual users subject the system to workload distributions that vary from the average
Given a machine model with a set of design variables whose values are unknown, the design and analysis task consists of determining consistent values for these variables to optimize
Performance
Cost of resulting design
Model based techniques develop declarative, multi-use models of machines with certain parameters open and only constrained by ranges
Using these models, design solutions can be analyzed considering Workload Distribution
Exact
Qualitative

14. Exact and Qualitative Workload Distributions
Model based scheduler determines and compares the productivity of a set of configurations
Qualitative
Compute the expected deviations of different configurations from optimal productivity
Determine a convex hull that shows which designs are best for which workload distributions

15. Implementation Solver Alternates Between
Adding Constraints
Searching for solutions
Committing to partial solutions
Solver manages relation between timing variables and real time
Full optimization with minimal commitment
Optimizer returns a solution for the next sheet only but guarantees it is part of a currently optimal solution for all known sheets

16. Solver Design
Low-level constraint operations should be incremental and distributed over time to minimize their efforts
Allows trade-offs between memory and processor usage
Scheduling algorithm should be able to make use of its application model to help the solver manage its resources efficiently

17. Real Time
Does not impose deadlines on jobs – there is always a feasible schedule
Always finds a solution in polynomial time for typical machines and jobs

18. Benefits Better Basis for
Reusability
Compositionality
Represent capability execution as discrete events with predictable durations and transport times
Enables qualitative and quantitative reasoning at design and run time

19. Benefits Models describe the local behaviors of components
States constraints and transformations on
Parts moving through the components
Constraints on the timing of resource allocation
Combination of symbolic, qualitative and numerical constraints and connecting component models to describe the entire machines
Reason about the behavior of the composite configuration
Enables a variety of applications