Reading List

(with references)

1. Introductory materials: Three techniques for performance evaluation -- analytical modeling, simulation and direct measurement. Performance metrics -- response time, throughput, efficiency, utilization, reliability, availability. Workloads -- synthetic, kernels, benchmarks. [RJ Chap 1-4]
2. Review of Probability and Statistics: Probability, random variable, sample space, conditional probability, independence, continuous and discrete random variables, probability mass function (pmf), probability distribution function (pdf), cumulative distribution function (cdf), expected value, variance, std. deviation, median, quantile. Common discrete and continuous distributions, properties of exponential and normal distributions. [RJ Chap 12, 29, plus a statistics book]
3. Parameter Estimation: Sample and population, goodness of estimation, standard error, interval estimation, confidence interval estimation for the mean using t and normal distributions, relative confidence interval, determination of suitable sample size.  Linear Regression [RJ Sections 13.1-5, 13.9, 14.1-4 plus a statistics book]
4. Random Number and Variate Generation: Pseudo-random numbers, uniform(0,1) generator, linear congruential (LCG) generator, multiplicative generator, prime modulus generator, period of a generator. Seed selection. Random variate generation using inverse transform technique, generating discrete distributions for a given prob. mass function.  [RJ 26.1-2, 26.6-7, 28.1]
5. Discrete Event Simulation: Notion of event, state and time, future event list and operations on it. Simulation of a single server queue, network of queues, central server systems. Use of the SMPL package [RJ Sections 25.3-5, handouts from MD book.]. Input and output analysis for simulations. Use of replications and batch means analysis. Fitting data to probability distributions.
6. Queuing Theory: Definitions and notations, single queue and queuing networks, queueing models of computer systems. Operational laws for queueing systems -- Little's law, utilization law, interactive response time law. Bottleneck analysis [RJ Chap 30, 33, QSP Chap 1-4 (particularly 3)]. Basic notions of stochastic processes, Markov chains - discrete and continuous time. Analysis of single Markovian queue. M/M/1, M/M/c, M/M/c/B etc. Queuing networks - open and closed. Product form networks. Examples from computer systems. Mean value analysis. [RJ Chap 31,32,34, handouts from KT Book].
7. Case Studies: Modeling local area networks (time permitting).