Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2019/2020
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 054657 - TRAFFIC THEORY
Docente Filippini Ilario
Cfu 10.00 Tipo insegnamento Monodisciplinare
Didattica innovativa 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

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

Argomenti trattati

Recap on Events and Random Variables

Introduction to Stochastic Processes

Arrival Processes: Bernoulli Processes, Poisson Processes, Extended Poisson Processes

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 


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.

Risorsa bibliografica obbligatoriaClass notes https://beep.metid.polimi.it/
Risorsa bibliografica facoltativaW. 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
Ore di studio autonome
Laboratorio Informatico
Laboratorio Sperimentale
Laboratorio Di Progetto
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
schedaincarico v. 1.6.1 / 1.6.1
Area Servizi ICT