Lecture 1 - Introduction to performance evaluation of computer systems

Lecture 2 - How to avoid common mistakes

Lecture 3 - Selection of techniques and metrics

Lecture 4 - Case study: Selection of techniques and metrics

Lecture 5 - Random Variables and probability distributions

Lecture 6 - Probability distributions - I

Lecture 7 - Probability distributions - II

Lecture 8 - Probability distributions - III

Lecture 9 - Stochastic process

Lecture 10 - Markov Chain

Lecture 11 - Slotted Aloha protocol model and discrete-time birth death process

Lecture 12 - Continuous time Markov chain and queuing theory - I

Lecture 13 - Queuing theory - I (Continued)

Lecture 14 - Queuing theory - II

Lecture 15 - Queuing theory - III

Lecture 16 - Queuing theory - IV

Lecture 17 - Queuing theory - V

Lecture 18 - Queuing theory - VI

Lecture 19 - Queuing networks - I

Lecture 20 - Queuing networks - II

Lecture 21 - Slotted Aloha Markov model

Lecture 22 - Simulations - I

Lecture 23 - Simulations - II

Lecture 24 - Simulations - III

Lecture 25 - Operational laws - I

Lecture 26 - Operational laws - II

Lecture 27 - Open and closed queuing networks

Lecture 28 - Approximate MVA

Lecture 29 - Convolution algorithm - I

Lecture 30 - Convolution algorithm - II

Lecture 31 - Load-dependent service centers

Lecture 32 - Hierarchical decomposition

Lecture 33 - Balanced Job Bounds

Lecture 34 - Confidence interval for propotions and introduction to experimental design

Lecture 35 - 2k factorial design

Lecture 36 - 2k r factorial design and 2k-p fractional factorial design

Lecture 37 - Programming aspects of discrete-event simulations - I

Lecture 38 - Programming aspects of discrete-event simulations - II

Lecture 39 - Discrete-event simulations - III

Lecture 40 - PetriNets - I

Lecture 41 - PetriNets - II

Lecture 42 - PetriNets - III