Moduł oferowany także w ramach programów studiów:
Informacje ogólne:
Nazwa:
Modelling and simulation of networks and services
Tok studiów:
2017/2018
Kod:
IES-2-233-NA-s
Wydział:
Informatyki, Elektroniki i Telekomunikacji
Poziom studiów:
Studia II stopnia
Specjalność:
Networks and Services
Kierunek:
Electronics and Telecommunications
Semestr:
2
Profil kształcenia:
Ogólnoakademicki (A)
Język wykładowy:
Polski
Forma i tryb studiów:
Stacjonarne
Strona www:
 
Osoba odpowiedzialna:
dr hab. inż. Szott Szymon (szott@kt.agh.edu.pl)
Osoby prowadzące:
prof. dr hab. inż. Papir Zdzisław (papir@kt.agh.edu.pl)
dr inż. Rusek Krzysztof (krusek@agh.edu.pl)
dr hab. inż. Szott Szymon (szott@kt.agh.edu.pl)
Krótka charakterystyka modułu

The aim of the module is to learn the methodology of research on the efficiency of telecommunications systems using analytical and simulation models.

Opis efektów kształcenia dla modułu zajęć
Kod EKM Student, który zaliczył moduł zajęć wie/umie/potrafi Powiązania z EKK Sposób weryfikacji efektów kształcenia (forma zaliczeń)
Wiedza
M_W001 Has in-depth knowledge of analytical modeling of network systems and services. Has in-depth knowledge of probability (random number generators) and statistics (central tendency, system variability, confidence intervals) needed to conduct simulation analysis of ICT networks and to develop simulation results. ES2A_W01 Egzamin,
Kolokwium
M_W002 Has structured knowledge in the field of analysis of wired and wireless ICT networks using analytical tools and using a discrete event simulator. ES2A_W03 Egzamin,
Kolokwium
M_W003 Has deepened and structured knowledge in the field of modeling and simulation of ICT networks and is able to compare the results obtained with experimental results. ES2A_W04 Egzamin,
Kolokwium
Umiejętności
M_U001 He can use technical documentation for simulation software. ES2A_U01 Projekt
M_U002 Is able to develop detailed documentation of the results of the simulation experiment, including justification of the adopted simplifications, configuration of simulation scenarios, description of statistical analysis and conclusions. ES2A_U03 Projekt
M_U003 Is able to carry out a simulation analysis of a teleinformation network taking into account the time of simulator warmup. ES2A_U07 Projekt
Kompetencje społeczne
M_K001 He is able to creatively solve problems related to the configuration and running of simulation scenarios and the processing of result data. ES2A_K01 Projekt
Matryca efektów kształcenia w odniesieniu do form zajęć
Kod EKM Student, który zaliczył moduł zajęć wie/umie/potrafi Forma zajęć
Wykład
Ćwicz. aud
Ćwicz. lab
Ćw. proj.
Konw.
Zaj. sem.
Zaj. prakt
Zaj. terenowe
Zaj. warsztatowe
Inne
E-learning
Wiedza
M_W001 Has in-depth knowledge of analytical modeling of network systems and services. Has in-depth knowledge of probability (random number generators) and statistics (central tendency, system variability, confidence intervals) needed to conduct simulation analysis of ICT networks and to develop simulation results. - - - + + - - - - - -
M_W002 Has structured knowledge in the field of analysis of wired and wireless ICT networks using analytical tools and using a discrete event simulator. - - - + + - - - - - -
M_W003 Has deepened and structured knowledge in the field of modeling and simulation of ICT networks and is able to compare the results obtained with experimental results. - - - + + - - - - - -
Umiejętności
M_U001 He can use technical documentation for simulation software. - - - + - - - - - - -
M_U002 Is able to develop detailed documentation of the results of the simulation experiment, including justification of the adopted simplifications, configuration of simulation scenarios, description of statistical analysis and conclusions. - - - + - - - - - - -
M_U003 Is able to carry out a simulation analysis of a teleinformation network taking into account the time of simulator warmup. - - - + - - - - - - -
Kompetencje społeczne
M_K001 He is able to creatively solve problems related to the configuration and running of simulation scenarios and the processing of result data. - - - + - - - - - - -
Treść modułu zajęć (program wykładów i pozostałych zajęć)
Ćwiczenia projektowe:
  1. Part 1. Simulation basics

    In the first part the student obtains knowledge about the following areas:
    1. Goal of modeling and simulation. Definitions. When simulation is the appropriate tool. Advantages/disadvantages of simulation. Areas of application. System terminology. Models of a system. Performance metrics. Characterizing a simulation model. Types of models and simulators. Simulation examples. Simulation study. Simulation languages and packages.
    2. Discrete-event model. Future event list. Event scheduling.
    3. Basics of random number generation. Generating random numbers from uniform and other distributions. Tests of uniformity tests and independence.
    4. Steady-state and transient simulations. Central tendency. System variability. Confidence intervals. Data collection and analysis techniques.
    5. Main simulation steps. Connections and applications. Random variables. Queuing models. Result processing and visualization. Confidence intervals – result validation.

  2. Part 2. Simulation analyses

    In the second part, the student performs a simulation analysis of a given network considering the techniques learned in part one.

Konwersatorium:

1. Modelling telecommunication networks
Modelling methods and purposes. Pareto rule in modelling. QoE and QoS metrics. Qos controls.

2. Exponential teletraffic models
Definition of session. Teletraffic and Qos controls on different time scales. Count, instantaneous, and fluid teletraffic models. Traffic intensity. Poisson counting model and instantaneous exponential model. Properties of the Poisson and exponential teletraffic models (meymorylessness, PASTA, inspection time paradox, binomial approximation). Multiphase exponential models. Renewal process.

3. Markov processes in teletraffic modelling
Poisson process as a birth process. Markov process as a random walk over graph. Evolution of Markov process (transient and stationary states). Decomposition of Markov process to sojourn times and embedded Markov chain. Semi-Markov processeses.

4. M/M/1 queue as a model of packet transmission
Kendall notation. Evolution of a M/M/1 queue (transient and stationary states). Solutions of M/M/1 stationary state equations. M/M/1 queue with state dependent parameters.

5. Little’s theorem. QoS metrics (throughput, delay, occupation) for M/M/1 queue. Optimum operating point of M/M/1 queue. M/M/1/N queue. M/G/1 queue (Pollaczek-Kchinchin theorem) and M/G/1 queue. Selfsimilar traffic and SS/M/1 queue.

6. Markovian open networks
Packet processing in networks (buffering, routing, multiplexing). Burke’s theorem. State probability distribution for open Markovian networks. Optimization of network metrics.

7. Markovian closed netowrks
Open M/M/1/N queue as a clsed queue. MVA algorithm. State probability distribution for closed Markovian networks. Optimization of window flow control. BCMP models.

Nakład pracy studenta (bilans punktów ECTS)
Forma aktywności studenta Obciążenie studenta
Sumaryczne obciążenie pracą studenta 120 godz
Punkty ECTS za moduł 4 ECTS
Udział w konwersatoriach 28 godz
Samodzielne studiowanie tematyki zajęć 50 godz
Udział w ćwiczeniach projektowych 14 godz
Wykonanie projektu 28 godz
Pozostałe informacje
Sposób obliczania oceny końcowej:

1. Aby uzyskać pozytywną ocenę końcową niezbędne jest uzyskanie pozytywnej oceny z ćwiczeń projektowych, konwersatorium oraz zdanie egzaminu.
2. Obliczamy średnią ważoną z ocen z ćwiczeń projektowych (30%), konwersatorium (30%) oraz egzaminu (40%) uzyskanych we wszystkich terminach.
3. Wyznaczmy ocenę końcową na podstawie zależności:
if sr>4.75 then OK:=5.0 else
if sr>4.25 then OK:=4.5 else
if sr>3.75 then OK:=4.0 else
if sr>3.25 then OK:=3.5 else OK:=3

Wymagania wstępne i dodatkowe:

Prerequisites:
1. Networking basics
2. Stochastic modelling
3. Probability and statistics

Zalecana literatura i pomoce naukowe:

Project classes:
1. G. Wainer, „Discrete-Event Modeling and Simulation”
2. K. Wehrle, M. Günes, J. Gross, „Modeling and Tools for Network Simulation”
3. M. Guizani, A. Rayes, B. Khan, A. Al-Fuqaha, „Network Modeling and Simulation: A Practical Perspective”
4. J. Banks, J. Carson, B. Nelson, D. Nicol, „Discrete-Event System Simulation”
Conversation seminar:
1. L. Kleinrock, “Queueing Systems – Vol. I: Theory”, John Wiley & Sons 1975
2. D. Bertsekas, R. Gallager, “Data Networks”, Prentice Hall 1993
3. L. Lipsky, “Queueing Theory – A Linear Algebraic Approach”, University of Connecticut, 2008
4. M. Zukerman, “Introduction to Queueing Theory and Stochastic Teletraffic Models”, EE Department, City University of Hong Kong

Publikacje naukowe osób prowadzących zajęcia związane z tematyką modułu:

1. S. Szott, M. Natkaniec i inni, “Evaluating New Concepts in Wireless Communications: From Theory to Practice”, European Wireless 2016; 22nd European Wireless Conference, 2016.
2. Z. Papir, “Ruch telekomunikacyjny i przeciążenia sieci pakietowych”, WKiŁ 2001
3. K. Rusek, L. Janowski, Z. Papir, “Transient and stationary characteristics of a packet buffer modelled as an MAP/SM/1/BSystem”, International Journal of Applied Mathematics and Computer Science, vol. 24 no. 2, 2014, s. 429–442.
4. J. Rachwalski, Z. Papir, “Burst ratio in concatenated Markov-based channels”, Journal of Telecommunications and Information Technology, nr 1, 2014, s. 3–9.

Informacje dodatkowe:

Brak