Computer networks modelling

Lectures:
1. Introduction (requests, servers, queues, communication path, cyclic process of simulation analysis)
2. Theory of service (Kendall's notation, Little's rule, utility factor, state space, time space, intensity of arrival and service, birth-death process, sample service units, priority service, service networks)
3. Petri and colored Petri nets (definition, marking tree, reachability of states, examples of models from the field of computer science and computer safeness, limitation, conservatism)
4. 4. Metrics and tools for evaluating network performance (latency, number of packet hops, energy efficiency)
5. Obtaining the values of quantitative variables of the network
6. Qualitative metrics for evaluating network performance
7. Traffic generation models in computer networks

Laboratory courses:
Methods and approaches presented during the lectures will be demonstrated on practical computer network examples during the laboratory courses. Different software tools will be used such as OpNet, NS2, OMNeT++, TETCOS, GTNetS, etc.

M. Mraz: Modeliranje računalniških omrežij (e-učbenik), 169 strani, https://ucilnica.fri.uni-lj.si/course/view.php?id=84, 2023
J.F.Shortle, J.M.Thomson, D.Gross, C.M.Harris: Fundamentals of queueing theory, John Wiley and Sons Inc., 2018
G.Giambene: Queueing theory and Telecommunications, Springer, 2021

Objective of the course is to present the basics in modelling and simulation of computer networks to the students of computer and information science. The course is based on the theory of service which acknowledges the students with the terms such as demands, serving units (resources), queues, bottlenecks etc. Students will learn the practical values of theoretical knowledge on the problems that arise in the field of computer networks.
Other competences:
Developing skills in critical, analytical and synthetic thinking.
Practical knowledge and skills of computer hardware, software and information technology necessary for successful professional work in computer and information science.
The ability to understand and solve professional challenges in computer and information science.
The ability to apply acquired knowledge in independent work for solving technical and scientific problems in computer and information science, the ability to upgrade acquired knowledge.

Knowledge and understanding:
Having the theoretical and methodological knowledge from the field modelling and simulations.
Understanding the importance of the field.

Application:
Application of methodological knowledge in design and support of various computer networks and their services.

Reflection:
Understanding the relations among theoretical knowledge and methodologies and practical problems from the field of computer networks.

Transferable skills – are not bound only to this course:
Students gain a new system perspective on the field of computer networks. This perspective opens new viewpoints such as data gathering and interpretation, problem identification and solving, critical analysis and synthesis.

Lectures and oral presentations of the subject. Seminal work on real-life examples and problems.

Continuing (homework, midterm exams, project work)
Final (oral exam)
grading: 5 (fail), 6-10 (pass) (according to the Statute of UL)

MOŠKON, Miha, PUŠNIK, Žiga, STANOVNIK, Lidija, ZIMIC, Nikolaj, MRAZ, Miha. A computational design of a programmable biological processor. Biosystems. Nov. 2022, vol. 221, str. 1-12.
REŽEN, Tadeja, MARTINS, Alexandre, MRAZ, Miha, ZIMIC, Nikolaj, ROZMAN, Damjana, MOŠKON, Miha. Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models. Computers in Biology and Medicine. [Print ed.]. Jun. 2022, vol. 145, str. 1-10.
MOŠKON, Miha, MRAZ, Miha. Programmable evolution of computing circuits in cellular populations. Neural computing & applications. Nov. 2022, vol. 34, iss. 21, str. 19239-19251.
WALAKIRA, Andrew, ROZMAN, Damjana, REŽEN, Tadeja, MRAZ, Miha, MOŠKON, Miha. Guided extraction of genome-scale metabolic models for the integration and analysis of omics data. Computational and Structural Biotechnology Journal. 2021, vol. 19, str. 3521-3530.
BORDON, Jure, MOŠKON, Miha, ZIMIC, Nikolaj, MRAZ, Miha. Semi-quantitative modelling of gene regulatory processes with unknown parameter values using fuzzy logic and Petri nets. Fundamenta informaticae. 2018, vol. 160, no. 1/2, str. 81-100.

Celotna bibliografija je dostopna na SICRISu.