Course Information

Course Name	Turkish	Markov Sistemlerin Modelleme ve Analizi
	English	Modeling & Analysis of Markovian Systems
Course Code	BBL 541E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	-	3	3	-	-
Course Language		English
Course Coordinator		Mehmet Akif Yazıcı
Course Objectives		1) To give the basics of Markov modeling through Memoryless and Markov properties 2) To teach modeling and analysis using Markov chains 3) To analyze various Markovian queueing systems and their generalizations 4) To show real life examples from the IT world that Markovian analysis is applicable
Course Description		Review of probability theory and random processes, memoryless property, Markov property, renewal processes, Poisson process, discrete- and continuous-time Markov chains, classification, properties, Chapman–Kolmogorov equations, steady-state solutions, birth-death processes, balance equations, Little's Law, M/M/1, M/M/k, M/M/k/k and M/M/k/k/N systems, Erlang formulas, Markovian arrival process (MAP) arrivals, phase-type service times, Aloha systems, other special topics
Course Outcomes		Students who pass the course will have knowledge about: 1) Memorylessness and Markov properties of random variables and processes, 2) Markov chains, 3) Analysis methods and characteristics of Markovian queueing systems, 4) The use of Markovian modeling and analysis methods in real life scenarios
Pre-requisite(s)
Required Facilities
Other
Textbook		Fayez Gebali. Analysis of Computer and Communication Networks. Springer, 2008
Other References		[1] Hisashi Kobayashi, Brian L. Mark. System Modeling and Analysis: Foundations of System Performance Evaluation. Pearson/Prentice Hall, 2008 [2] Sheldon. M. Ross. Introduction to Probability Models. Academic Press, 11th edition, 2014 [3] Leonard Kleinrock, Queueing Systems: Volume I. Wiley-Interscience, 1975 [4] Mor Harchol-Balter. Performance Modeling and Design of Computer Systems: Queueing Theory in Action. Cambridge University Press, 2013

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