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

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

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

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

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

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

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

(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

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

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

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

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

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

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

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

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

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

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

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