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INFO4110 : Performance Evaluation

Performance Evaluation

Niveau M1+M2

Discipline(s) Informatique

ECTS 6.00

Période 1e semestre

Localisation Site Monod

Année 2021-2022

 Public externe (ouverts aux auditeurs de cours)
 

Objectif du cours

"Performance evaluation" is a label that covers many topics, from theory to practice, in computer science and computer engineering. It includes the design of mathematical models (very often with probabilistic assumptions), the analysis of such models (mathematically or using simulation), the design of experimental protocols (measuring tools and data collection), the analysis of data (statistics on real experiments or in silico experiments). It is used to design, compare or tune computer systems and communication networks. Some tools also apply to related systems like transport networks, logistics or automatic control.

Prerequisites:

  • Basic knowledge in probability and in Python (or R) programming (for statistics)
  • Intermediate level in Linux Operating System, including command lines and C programming (for practical case study)

 

Grading: 50% continuous assessment (at least one homework assignment and one time-limited written test), 50% final exam (time-limited written exam).

Performance Evaluation (from theory to practice)

  • Performance Evaluation of Computer and Communication Systems, by J.-Y. Le Boudec. EPFL Press, 2011.
  • The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling, by R. Jain. Wiley-Interscience, 1991.
  • Systems Performance: Enterprise and the Cloud, by B. Gregg, Addison-Wesley, 2013.

Probability theory

  • An Introduction to Probabilistic Modeling, by P. Brémaud. Springer-Verlag, 1994.
  • Markov Chains, Gibbs Fields, Monte Carlo Simulation and Queues, by P. Brémaud. Springer-Verlag, 1999.

Queuing theory

  • Queuing Systems : Theory (vol I) & Computer Applications (vol II), by L. Kleinrock. John Wiley & Sons Inc, 1976.
  • Elements of Queueing Theory: Palm Martingale Calculus and Stochastic Recurrences, by F. Baccelli & P. Brémaud. Springer-Verlag, 2000.

Statistics

  • The Cartoon Guide to Statistics, by L. Gonick & W. Smith. William Morrow Paperbacks, 1993.
  • Python for Data Analysis, by W. McKinney. O′Reilly, 2012.
Modifié le :
05/09/2021 17:20:42