Syllabus

Introduction to Probability Models and Simulation: Probability space, Random variables, Discrete and Continuous distribution: uniform, geometric, exponential, normal distribution etc, System Modeling, Measurement techniques, Experimental design, workload design, Simulations, Data Analysis and Visualization Basics of Modeling: Performance metrics: Bandwidth utilization, throughput, delays, error rate, network reliability etc. Poisson process, and Markov chain theory. Queuing Theory: Arrival and service processes, Server disciplines, Queuing networks: Open vs closed networks, M/M/1, M/M/1/K, M/M/m, M/M/m/m. Simulation and Analysis of Computing Systems: time averages versus ensemble averages, Asymptotic bounds and limit theorems, confidence intervals, generating random variables for simulation, Inspection Paradox.

Employ probability theory and computational statistics to analyze system performance.

Identify system-specific metrics to evaluate the processor, database, network and server workload performance.

-Inspect and examine the outcome of experiments using various approaches or techniques.
-Select and interpret appropriate evaluation techniques, performance metrics and workloads for a system

Sheldon M. Ross, Introduction to Probability Models, Academic Press , 7th Edition

R. Jain, The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation and Modeling, John Wiley & Sons,

Kishor S. Trivedi, Probability and Statistics with Reliability, Queueing, and Computer Science Applications,, Wiley

Sanjay K. Bose, An Introduction to Queuing System, Springer

IEEE Conferences

IEEE Journal