Performance Evaluation of Computer Systems and Networks
(Subjects are not necessarily in order of presentation).
- Probability theory and statistics (~40 h):
- Fundamental definitions and theorems on probability. Uniform probability model. Discrete and continuous random
variables. Notable RV distributions (exponential, uniform, Poissonian, normal, binomial, chi-square, student-t etc.). Central limit theorem.
- Sample and population: estimators and confidence intervals. Data analysis and summarization. Model fitting, experiment design.
- Simulation (~25 h):
- Principles of discrete event simulation: events, event queues, random number generation, structure of a simulator software.
- Description of the general-purpose OMNET++ simulation framework. Hands-on experiments with the OMNET++ framework.
- Simulation workflow: system modeling, experiment planning, factor reduction, independent replications, transient and steady-state behavior, output data analysis, experiment automation.
- Queueing Theory (~25 h):
- Queueing theory: Markov Property, Poisson processes, birth/death systems. Transient and steady-state solutions. Single-queue systems, open and closed queueing networks. Classed networks. Processor-sharing systems.
Students will also be requested to prepare
a term project, consisting of both documentation and code, related to modeling
and simulative performance analysis of a system.
Students may also find useful:
- S.M. Ross “Introduction to Probability and Statistics
for Engineers and Computer Scientists”, Elsevier
- R. Jain “The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement,
Simulation, and Modeling”
- L. Kleinrock, Queueing Systems, vol. 1, Wiley
- J.Y. Le Boudec “Performance Evaluation of Computer and Communication Systems”, EPFL
- D.A. Menascé et al. “Performance by Design”, Prentice Hall
- A.M. Law, W.D. Kelton “Simulation Modeling and Analysis”, McGraw-Hill
- S.M. Ross, "Introduction to Probability Models", Elsevier