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Course details 2012-2013  
    
Advanced Performance Modelling
Course Code :2001WETAPM
Study domain:Computer Science
Semester:Semester: 1st semester
Contact hours:45
Credits:6
Study load (hours):168
Contract restrictions: No contract restriction
Language of instruction :English
Exam period:exam in the 1st semester
Lecturer(s)Benny Van Houdt

 


1. Prerequisites

At the start of this course the student should have acquired the following competences:
An active knowlegde of :
  • English
A passive knowledge of :
  • English
Specific prerequisites for this course:
Some basic knowledge about Markov chains, provided by the course Introduction to Performance Modelling is required.


2. Learning outcomes

The students become acquainted with some more advanced stochastic modeling techniques and learn how to apply these techniques using various examples. The following subjects are discussed in this course: phase-type distributions, Markov arrival processes, Quasi-Birth-Death, M/G/1-type and GI/M/1-type Markov chains. Advanced algorithms used to solve non-linear matrix equations are also discussed as a means to determine the steady state performance measures of these Markov chains. Students should also be able to develop stochastic models for problem situations that resemble those discussed during the course. Finally, students also gather experience in the use of the associated software tools. 


3. Course contents

The following subjects form a central part of this course: phase-type and matrix exponential distributions, Markov arrival processes, Quasi-Birth-Death, M/G/1-type and GI/M/1-type Markov chains (with finite and infinite state spaces), iterative algorithms to solve non-linear matrix equations (e.g., functional iterations, cyclic reduction, logarithmic reduction, Newton iteration, etc.). Software tools are also discussed.


4. Teaching method

Class contact teaching:
  • Lectures

  • Personal work:
  • Assignments:Individually

  • Project-based work:
  • Individually



  • 5. Assessment method and criteria

    Continuous assessment:
  • Assignments

  • Written assignment:
  • Without oral presentation


  • 6. Study material

    Required reading

    All the slides (about 200) will be made available via Blackboard.


    Optional reading

    The following study material can be studied on a voluntary basis:
    Introduction to Matrix Analytic Methods in Stochastic Modeling,
    G. Latouche and V. Ramaswami
    ISBN-10: 0898714257


    7. Contact information
    For questions and remarks, please contact Benny Van Houdt  in room G222 (after making an appointment by email).
    (+)last update: 30/04/2012 12:02 benny.vanhoudt