Probability, random variables and their properties, mathematical expectation, specific discrete and continuous random variates (Poisson, exponential, etc.). Simulation tools, random number and variate generation, event serialisation and time advance algorithms; process and resource classes, Performance measures, model instrumentation and result presentation. Simple stochastic processes – discrete time Markov chains, continuous time Markov processes; Poisson process, Birth and Death process and their application to the simple (e.g. M/M/1) queues. More advanced queuing theory – multi-server queues, non-Markovian queues, networks of queues. mean value analysis (analytic derivation of throughput, utilisation, mean queue size and delay). Applications – case studies in computer systems and networks using analysis and simulation, advanced simulation software.