Performance Evaluation of Computer Systems


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