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SBSE

SBSE. Course 4. 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. Applications: Design. Multiprocessor scheduling

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SBSE

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  1. SBSE Course 4

  2. 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

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

  4. Design

  5. 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

  6. ◙A Multiprocessor Scheduling Scheme Using Problem-Space Genetic Algorithms • Nodes: tasks • Edges: communication • Weights: cost of communication

  7. Solution • Discussion: possible approaches

  8. ◙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

  9. ◙ 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

  10. Discussion of techniques

  11. Other articles • Pareto-based Soft Real-Time Task Scheduling in Multiprocessor Systems • Discussion of Pareto: multi-objective optimization • Discussion of elitism

  12. 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

  13. Hardware/software co-design in embedded systems • Hardware and software (designers) must cooperate • Hardware/software co-design

  14. Implementation

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

  16. ◙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

  17. ◙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 • -> see modern ADF-s

  18. ◙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”.

  19. 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.

  20. 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

  21. 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

  22. 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

  23. 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. • Don’t like any? Find your own SBSE article on the net and talk to me about it.

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