SBSE

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SBSE

• Course 4

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Overview Design

• Translate requirements into a representation of
  software
• Focuses on
• Data structures
• Architecture
• Interfaces
• Algorithmic details
• Also include
• Resource and task allocation in a distributed
  system

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Applications Design

• Multiprocessor scheduling
• Task and resource allocation in distributed
  systems
• Hardware/software co-design in embedded systems
• Protocol construction
• Architecture design

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Design
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Multiprocessor scheduling

• Context multiprocessor system
• Goal minimize total run time of the task
• Multiprocessor scheduling static/dynamic
• Consider communication
• Message passing
• Waiting / possible deadlocks
• MPS (s/d) a.k.a. Load Balancing

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? A Multiprocessor Scheduling Scheme Using
Problem-Space Genetic Algorithms

• Nodes tasks
• Edges communication
• Weights cost of communication

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Solution

• Discussion possible approaches

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? A Modified Genetic Algorithm for Task
Scheduling in Multiprocessor Systems

• Topic Multiprocessor scheduling
• Approach Partitioned GA-s
• Divide and conquer
• Divide the problem into subproblems, solve them,
  then combine them
• Task graph is partitioned
• Results better than the classical GA

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? Efficient Scheduling of Arbitrary Task Graphs
toMultiprocessors using A Parallel Genetic
Algorithm

• Uses a parallel implementation of GA
• Examples of approaches
• Parallel evaluation
• Parallel evolution/ island model

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Discussion of techniques
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Other articles

• Pareto-based Soft Real-Time Task Scheduling in
  Multiprocessor Systems
• Discussion of Pareto multi-objective
  optimization
• Discussion of elitism

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Task and resource allocation in distributed
systems

• Data, functionality usually allocated to specific
  nodes
• Node may need resources it owns or shared
  resources
• Inter-node communication may cause delays and
  reliability problems

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Hardware/software co-design in embedded systems

• Hardware and software (designers) must cooperate
• Hardware/software co-design

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Implementation
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Implementation

• Automatic programming, N-version programming,
  search for compiler optimisations and
  re-engineering
• Review GP techniques

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? Automatic generation of object-oriented
programs using genetic programming

• Generation of OO-programs
• Memory zone allocated for data
• Methods are supposed to learn to operate properly
  on the attributes
• Application
• Evolved structures like stack, queue using
  primitive operations

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? Automatic programming and program maintenance
withgenetic programming

• Turing programs are evolved my means of GP
• Once such program solves a problem, it is added
  to a library of reusable code snippets
• -gt see modern ADF-s

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? Generating Multiple Diverse Software Versions
with Genetic Programming

• A variant for N-Version programming
• Start more GP processes, each with its specific
  settings, aiming to synthesize new ideas.

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Steps

• 1. The design and implement fitness function by
  using software specification.
• 2. Decide, which GP system parameters will be
  varied.
• 3. Decide how parameters will vary.
• 4. Choose the parameter value combinations to use
  in GP runs.
• 5. Let GP system to run for each combination of
  parameter values.
• 6. Measure fitness for each generated GP program.
  Calculate the diversity.
• 7. Select the program combinations with lowest
  failure probability to the software fault
  tolerance structure.

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Re-engineering

• A program becomes too old, to cracky, needs to
  be refurbished.
• ? Automatic re-engineering of software using
  genetic programming
• Program parallelisation
• ? Genetic algorithm based restructuring of
  object-oriented designs using metrics
• Restructure OO design

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Search for compiler optimizations

• ? Optimizing for Reduced Code Space using Genetic
  Algorithms
• Each optimization method is given a letter, the
  problem is to find which is the better sequence
  of optimizations to apply
• ? GAPS A Compiler Framework for Genetic
  Algorithm (GA) Optimised Parallelisation

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Lab tasks (optional)

• Parameter control in Genetic Algorithms
  dwindling mutation rate
• Multi-objective optimization
• As many 1001-s as possible
• As many 11101-s as possible
• Smart bug (GP application)
• Textual notation for UML research
• http//www.infoiasi.ro/ogh/tep

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Tasks

• Read the survey
• Skim over the articles
• Like one? Choose it!
• You are not supposed to like a 2-page article,
  unless you can implement the techniques described
  in it.
• Dont like any? Find your own SBSE article on the
  net and talk to me about it.