Network Traffic Engineering

Professor: Andrea Baiocchi

Degree in: 
  Master of Communication Engineering
  Master of Computer Engineering
  Master of Industrial Engineering
  Master of Data Science
  Master of Artificial Intelligence and Robotics
If your curriculum does not appear in the list above, please contact the lecturer.
Semester: I

Lectures Time

- Wed, 2 pm - 5 pm, Classroom DIET09 (close to classroom 9), Via Eudossiana 18.
- Thu, 2 pm - 4 pm, Classroom DIET09 (close to classroom 9), Via Eudossiana 18.

Office hours

Monday, from 11:30am to 12:30pm; DIET department 1° floor, office n°107

Prerequisite

Undergraduate level knowledge of probability and telecommunications networking. Basic computer programming skills.

Outline of the Course

The classes are organized into two main parts. About 2/3 of class time is devoted to face-to-face lessons and examples/applications/exercises. The remaining 1/3 of the class time is devoted to stochastic simulation laboratory.
The syllabus of the course is as follows.

Traffic engineering.(4 hours) Role of and approaches to performance evaluation and network traffic engineering. Service systems: definitions and structure. Arrival and service processes. Traffic process. Performance metrics. Lindley’s recursion. Little’s law.

Scheduling and Load Balancing. (9 hours) Classification and conservation law. Priority: HOL, SJF. Scheduling: Processor Sharing and Generalized Processr Sharing, Round Robin, Credit-based scheduling, LAS. Load Balancing: push and pull policies, delay optimality, JSQ, JBT.

Packet networks. (6 hours) Jackson open queueing networks. Analysis of transit time at equilibrium. Applications to an IP network. Optimization of link capacities for a given routing. Braess’ paradox.

Cellular networks. (6 hours) Erlang’s model. Applications to cellular networks. Dimensioning of the cellular coverage under quality of service constraints. Comparison of Fixed versus Dynamic Channel Allocation.

Random access protocols. (6 hours) Slotted ALOHA: protocol, model, analysis, stabilization. Wi-Fi CSMA/CA: back-off model, saturation throughput analysis. Performance anomaly..

Congestion control. (9 hours) Fluid approximation. Examples. TCP refresher. Fluid analysis of the congestion control of a long-lived Reno TCP connection with a single bottleneck. DCTCP. Fluid analysis of DCTCP. Fairness and Network Utility Maximization (NUM). Interpretation of TCP congestion control as an adaptive, distributed controller solving NUM.

Laboratory of simulation. (20 hours). Service systems discrete simulation: event-driven and synchronous approaches. Generation of random variables. Basics of point and interval statistics. Development of simple models of stochastic simulation.

Course Object

These classes aim at providing tools and application examples for the performance evaluation and the dimensioning of service systems and specifically communication networks and protocols. Besides an introduction to models and to some applications, classes aim to point the student at tracks for autonomous in-depth study.
The course aims at making the student able to state and solve a performance evaluation problem, including possibly measurements, simulations, data analysis code. Through mini-projects and class lab students are encouraged to move from a possibly partial system description to a mathematical model defined to answer to quantitative issues on the system working. Specific attention is focused on a critical review of numerical results and to validity checking of hypotheses and approximations introduced in the models.

Course Description

The fact sheet of the course and the class schedule with the calendar can be found  here.

Final Exam

The exam consists of:
i) Homework assigned during clases..
ii) Simulation mini-project, to be developed in small groups (2-3 students).

Teaching Material

The teaching material can be downloaded from here.

Bibliography

- Main reference:

• A. Baiocchi, Network Traffic Engineering - Stochastic models and applications, Wiley, 2020.
Further references:
• F. Kelly and E. Yudovina, Stochastic networks, Cambridge Univ. Press, 2014.
• N. Gautam, Analysis of queues - Methods and applications, CRC Press, 2012.
• R. Srikant, The Mathematics of Internet Congestion Control, Birkhauser, December 2003.