This course covers the application of probability theory to computer networks. Random processes, Little's theorem, birth-death processes, Markov chains, Multidimensional Markov chains, M/M/1, M/M/m, M/M/m/m, M/G/1 and G/G/1 queuing systems and their applications in computer networks.
Loss models such as Erlang loss model and Engset loss model, Insensitivity and Generalization of loss models. Conservation laws, priority queues, and polling models. Traffic models such as Markovian traffic models and Long-Range Dependent (LRD) traffic models. Discrete event simulations, generation
of random variables, variance reduction techniques and general purpose simulation languages.