First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
ICT FOR INTERNET AND MULTIMEDIA
Course unit
NETWORK MODELING
INP3049939, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
ICT FOR INTERNET AND MULTIMEDIA
IN2371, Degree course structure A.Y. 2017/18, A.Y. 2018/19
N0
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Degree course track Common track
Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination NETWORK MODELING
Department of reference Department of Information Engineering
E-Learning website https://elearning.dei.unipd.it/course/view.php?idnumber=2018-IN2371-000ZZ-2018-INP3049939-N0
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge MICHELE ZORZI ING-INF/03

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP3049939 NETWORK MODELING MICHELE ZORZI IN0521
SCP8082659 NETWORK MODELLING MICHELE ZORZI SC2443

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/03 Telecommunications 9.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 9.0 72 153.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Examination board
Board From To Members of the board
2 A.A. 2018/2019 01/10/2018 15/03/2020 ZORZI MICHELE (Presidente)
ZANELLA ANDREA (Membro Effettivo)
BADIA LEONARDO (Supplente)
CALVAGNO GIANCARLO (Supplente)
CORVAJA ROBERTO (Supplente)
ERSEGHE TOMASO (Supplente)
LAURENTI NICOLA (Supplente)
ROSSI MICHELE (Supplente)
TOMASIN STEFANO (Supplente)
VANGELISTA LORENZO (Supplente)
1 A.A. 2017/2018 01/10/2017 15/03/2019 ZORZI MICHELE (Presidente)
ZANELLA ANDREA (Membro Effettivo)
BADIA LEONARDO (Supplente)
CALVAGNO GIANCARLO (Supplente)
CORVAJA ROBERTO (Supplente)
ERSEGHE TOMASO (Supplente)
LAURENTI NICOLA (Supplente)
MILANI SIMONE (Supplente)
ROSSI MICHELE (Supplente)
TOMASIN STEFANO (Supplente)
VANGELISTA LORENZO (Supplente)
ZANUTTIGH PIETRO (Supplente)

Syllabus
Prerequisites: The course requires preliminary knowledge of: Mathematical Analysis, Probability, random variables and random processes, networks and protocols. For the examples treated, a basic course in networks and protocols is useful (through not required).
Target skills and knowledge: The training objective of the course involves the acquisition of the following knowledge and skills:

1. To understand and know how to use probability theory and random processes to model real systems and evaluate their performance.
2. To acquire advanced analytical tools for the performance assessment of systems and networks
3. To know how to translate a problem into a corresponding mathematical model
4. To know which performance metrics can be calculated (and how) from a mathematical/probabilistic representation
5. To be able to state precisely and to prove rigorously the most important theoretical results related to the main topics of the course (Markov chains, Poisson processes, renewal processes)
Examination methods: The assessment of the knowledge and skills acquired is carried out by means of a written test divided into two parts.

Part A, with a duration of 90 minutes and open-book, consists of eleven numerical questions grouped into four exercises. Each question has a value of three points.

Part B, with a duration of 60 minutes and closed-book, consists of three theoretical questions (typically proofs of theorems seen in class). Each question has a value of eleven points.

If the student scores at least 15 points in part A and the average score of part A and part B is at least 18, the latter can be accepted as the final grade. If the score in part A is less than 15 or the average of the two tests is less than 18, the exam is not passed.

Even if the final exam can be passed by a successful written exam (in two parts), the student can always ask to take an oral exam if he/she wants to improve the grade. In no case can the oral exam replace the written test.

Examples of exams are available on the elearning platform course website, and are extensively covered in class.
Assessment criteria: The evaluation of the acquired knowledge and skills will be carried out considering:

1. The completeness and depth of the knowledge of the topics covered during the course.
2. The ability to model a problem using one of the analytical tools seen in class
3. The ability to obtain correct numerical results in the proposed exercises
4. The ability to develop analytical reasoning in a rigorous and complete manner.
Course unit contents: 1. Review of probability and random processes
2. Markov chains: definitions and main results
3. Markov chains: asymptotic behavior
4. Study of multi-access systems and their stability properties
5. Poisson processes: definitions and main results
6. Renewal processes: definitions and main results, asymptotic behavior
7. Renewal reward, regenerative, and semi-Markov processes
8. Exercises and examples of applications

A detailed list of the topics covered during the course, with specific reference to chapters and pages of the texts, is available on the course website through the e-learning platform.
Planned learning activities and teaching methods: Teaching is done through lectures on the blackboard, as it is believed that this method of delivery maintains the right presentation pace and keeps the students' attention, allowing interaction and involvement.

In order to verify the level of learning during the course, the students are proposed exercises or theoretical developments to be done at home, which will then be often carried out in class during a subsequent lecture.
Additional notes about suggested reading: The course follows a main textbook, with additions from other texts, notes and research articles.

With the exception of the main textbook, all the other teaching materials are made available to students on the elearning platform course website, including examples of exams and a list of proposed exercises from the text (with solutions).
Textbooks (and optional supplementary readings)
  • H. Taylor, S. Karlin, An introduction to stochastic modeling. --: Academic Press (3rd or 4th edition), 1998. TESTO PRINCIPALE/PRIMARY TEXTBOOK Cerca nel catalogo
  • S. Karlin, H. Taylor, A first course in stochastic processes. --: Acedemic Press (2nd ed.), 1975. Cerca nel catalogo
  • D. Bertsekas, R. Gallager, Data Networks. --: Prentice-Hall (2nd ed.), 1992. Cerca nel catalogo
  • S. Ross, Stochastic processes. --: Wiley (2nd ed.), 1996. Cerca nel catalogo
  • S. Ross, Applied probability models with optimization applications. --: Dover (2nd ed.), 1996. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Problem based learning
  • Interactive lecturing
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)

Sustainable Development Goals (SDGs)
Quality Education Industry, Innovation and Infrastructure