ARCHITECTURES AND APPLICATIONS FOR TLC NETWORKS MODULE 2

ARCHITECTURES AND APPLICATIONS FOR TLC NETWORKS MODULE 2

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iten
Code
72302
ACADEMIC YEAR
2016/2017
CREDITS
5 credits during the 2nd year of 9271 MULTIMEDIA SIGNAL PROCESSING AND TELECOMMUNICATION NETWORKS (LM-27) GENOVA
SCIENTIFIC DISCIPLINARY SECTOR
ING-INF/03
LANGUAGE
Inglese
TEACHING LOCATION
GENOVA (MULTIMEDIA SIGNAL PROCESSING AND TELECOMMUNICATION NETWORKS)
semester
2° Semester
modules
Teaching materials

OVERVIEW

This part of classes aims at introducing the basic techniques for performance evaluation of Telecommunication Networks and the models that are at the basis of Traffic Engineering in the Internet. It includes Markov Chain models and elements of queueing theory.

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims at providing theoretical and practical knowledge about telecommunication network architectures, methodologies for Quality of Service, modelling, and performance evaluation. The objective of the second part (M2) is to enable the student understand Markov chains and the basics of queueing theory applied to the modelling of telecommunication networks and of the Internet in particular.

LEARNING OUTCOMES (FURTHER INFO)

At the end of this part of the course, the student should be able to:

  • Model simple networking problems in terms of discrete- or continuous-time Markov chains, and find the stationary probability distribution and related performance indicators;
  • Model and solve networking problems involving birth-death (Markovian) queues, with single or multiple servers, finite or infinite buffers;
  • Model and solve networking problems involving service times with general statistical distributions, with or without server vacation;
  • Understand more complex queueing models with realtively little effort;
  • Use analytical modelling as another possible tool, besides simulation and measurements, in the design, management and control of telecommunication networks and in traffic engineering problems.

Teaching methods

Combination of traditional lectures (40 hours), and laboratory experience in matching modeling and measurements

SYLLABUS/CONTENT

  • Methods of network performance evaluation: analytical models, simulation, experimental measurements
  • Packet-level and flow-level models
  • Elementary queueing theory: elements of a queue, statistics of input and service, general results on infinite- and finite-buffer queues, Little’s Theorem, Kendall’s notation
  • Markovian queues: Poisson arrivals, exponential distribution, stationary distribution of general birth-death systems; M/M/1, M/M/1/K, M/M/m/m, M/M/m
  • Discrete- and continuous-time Markov Chains
  • M/G/1 and Pollaczek-Kinchin formula; Pareto distribution; M/G/1 with vacations; priority queueing
  • Networks of queues: Jackson networks, independence hypothesis, Kleinrock’s delay formula

 

RECOMMENDED READING/BIBLIOGRAPHY

 

Course material on Aulaweb  (https://www.aulaweb.unige.it): F. Davoli, “Lecture Notes for the Courses of Telecommunication Networks – Queueing Theory and Teletraffic”.

The parts of the notes required for this class are i) Basic queueing theory, including Markovian and M/G/1 queues, server vacation, priority queueing, and the contents of the two appendices on Markov Chains and on the Pareto distribution, along with applications to various telecommunication systems; ii) Jackson queueing networks and Kleinrock's delay formula.

TEACHERS AND EXAM BOARD

Ricevimento: Appointment upon students' requests.

Exam Board

MARIO MARCHESE (President)

FRANCO DAVOLI (President)

IGOR BISIO (President)

LESSONS

Teaching methods

Combination of traditional lectures (40 hours), and laboratory experience in matching modeling and measurements

LESSONS START

2nd semester 2016/17.

ORARI

L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.

Vedi anche:

ARCHITECTURES AND APPLICATIONS FOR TLC NETWORKS MODULE 2

EXAMS

Exam description

Written and oral (discussion on the results of the written test); periodic evaluation during the class (written tests).

Assessment methods

Solution capacity of simple modeling and performance evaluation problems.