L'insegnamento prevede 1.0 CFU erogati con Didattica Innovativa come segue:

Blended Learning & Flipped Classroom

Corso di Studi

Codice Piano di Studio preventivamente approvato

Da (compreso)

A (escluso)

Insegnamento

Ing Ind - Inf (Mag.)(ord. 270) - MI (474) TELECOMMUNICATION ENGINEERING - INGEGNERIA DELLE TELECOMUNICAZIONI

*

A

ZZZZ

054657 - TRAFFIC THEORY

Obiettivi dell'insegnamento

The goal of the course is to provide students with the methodologies needed to model, design, and analyze the performance of telecommunication networks and distributed IT systems, which are the main building blocks of the Internet. The course will provide a set of tools to manage stochastic processes involved in data traffic systems (traffic sources, scheduling policies, servers, and resource allocation, etc.), which can be applied to any technology: from traditional to the most advanced ones.

Students will learn and practice how to 1) describe real systems with theoretical tools and estimate/predict main perfomance figures, 2) design telecommunication systems and networks according to specific performance indicators. Potential and limits of the modeling tools will be analyzed, eventually resorting to system-level simulations. The principles of discrete-event simulation will be studied and applied to a full-fledged network simulator, OMNet++, used both in academic and industrial research laboratories.

Some advanced topics will be carried out with a flipped-classroom approach. The instructor will provide a mix of ad-hoc study material (class notes, slides, video, web pages, etc.) that groups of students have to exploit to prepare a short presentation for an open discussion. This activity is not mandatory.

Risultati di apprendimento attesi

Dublin Descriptors of this course

The student will learn to ...

Knowledge and understanding

Identify and describe stochastic processes

Describe Arrival Processes

Describe Markov Chains and Regenerative Processes

Describe traffic in ITC systems

Classify blocking, queueing and sharing serving systems

Describe the main performance indicators of serving systems

Describe networks of queues and distinguish their types

Indicate the main assumptions of Discrete-Event Simulation

Identify the main building blocks of a network simulator

Applying knowledge and understanding

Compute the main properties of Arrival Processes

Compute the main properties of Markov Chains and Regenerative Processes

Compute the main performance indicators of serving systems

Compute the main performance indicators of networks of queues

Run a complex network simulator (i.e., OMNet ++)

Develop a stochastic model of a real non-deterministic system (device, network, server, etc.)

Making judgements

Distinguish the main reasons behind the performance of a serving system or a network

Break down telecommunication networks and IT systems into blocks of serving systems

Predict the performance of telecommunication networks and IT systems

Recommend the best actions to improve the performance of telecommunication networks and IT systems

Evaluate the suitability of a serving system for the assigned task and expected performance

Judge whether to resort to analytical models or simulation to analyze the performance of a serving system

Markov Chains: Definitions, Extended Markov Chains, Probability distributions, continuous-time vs discrete-time

Regenerative Processes and Ergodicity

Models of Traffic Sources

Blocking Systems: classification and Erlang formulas

Markovian Queueing Systems: properties, M/M/1 and M/M/c queues, M/M/1/k and M/M/c/k queues

Processor Sharing Systems

Elements of M/G systems: General service time, Pollaczek–Khinchine formula

Advanced Queueing Systems: Service disciplines and traffic priorities

Queueing Networks: Burke’s Theorem, open and closed Jackson networks, advanced queueing networks

Discrete-event simulations: Introduction to discrete-event simulation and OMNET++ simulator

Prerequisiti

Students are expected to have a basic knowledge of Probability Theory and MATLAB. Knowledge of C / C ++ programming languages is recommended.

Modalità di valutazione

The assessment will be based on:

- written exam at the end of the course with 2/3 numerical exercises and 2/3 short questions, mandatory (up to 28 points)

- presentation in open discussion for groups of 2/3 peopole, non-mandatory activity (up to 3 points)

- small project in OMNeT++ for groups of 2/3 people, non-mandatory activity (up to 3 points)

The final score is the sum of the scores obtained in the three activities. In order to get a sufficient score, the written test must be >= 16. The mark "30 cum laude" will be assigned when the total score is greater or equal to 31.

Bibliografia

Class noteshttps://beep.metid.polimi.it/W. Stewart, Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling, Editore: Princeton University, Anno edizione: 2009, ISBN: 978-0691140629

Forme didattiche

Tipo Forma Didattica

Ore di attività svolte in aula

(hh:mm)

Ore di studio autonome

(hh:mm)

Lezione

60:00

90:00

Esercitazione

28:00

42:00

Laboratorio Informatico

10:00

6:00

Laboratorio Sperimentale

0:00

0:00

Laboratorio Di Progetto

2:00

12:00

Totale

100:00

150:00

Informazioni in lingua inglese a supporto dell'internazionalizzazione

Insegnamento erogato in lingua
Inglese

Disponibilità di materiale didattico/slides in lingua inglese

Disponibilità di libri di testo/bibliografia in lingua inglese

Possibilità di sostenere l'esame in lingua inglese

Disponibilità di supporto didattico in lingua inglese