 |
|
 |
Institute of Communication Networks and Computer Engineering (IKR)
|
 |
Project description |
|
|
 |
 |
 |
|
 |
 |
 |
Master thesis No. 1063
(Offer) [pdf]
|
Designing and Implementing Graph Neural Networks for Efficient IP-Optical Networking
|
|
|
Methods
|
Topics
|
Graph Neural Networks
Machine Learning
Network Modeling
Simulation
|
Multi-layer networks
Network control
|
|
|
Description
|
|
|
|
|
|
|
Background
|
Today, communication networks have evolved into complex systems handling vast amounts of data, making efficient management and optimization crucial. The network topology and state can be naturally represented as a graph structure, with nodes representing network elements and edges representing connections. Graph Neural Networks (GNNs) have emerged as a powerful tool for learning from graph-structured data, making them particularly suitable for network optimization problems. The Routing and Spectrum Assignment (RSA) problem in IP-optical networks presents a significant challenge that could benefit from GNN-based approaches, as it requires understanding both topology and state information.
|
Problem Description
|
In the context of this thesis, you are called to apply GNNs to solve network optimization problems. More specifically, the thesis consists of the following steps:
|
•
|
Familiarize yourself with Julia and related GNN packages
|
•
|
Design appropriate graph representations for network states
|
•
|
Implement and train GNN models for the RSA problem
|
•
|
Compare different GNN architectures and aggregation functions
|
•
|
Evaluate the impact of GNNs on solving the RSA problem
|
Acquired Knowledge and Skills
|
In this thesis, you will gain deep understanding of Graph Neural Networks and their application to networking problems. You will experiment with the scientific programming language Julia and its graph learning libraries. Additionally, you will get great insight into networking and network services.
|
|
|
|
Requirements
|
Desirable knowledge
|
Programming Experience
Communication Networks Architecture and Design
Basic Machine Learning Knowledge
|
Programming Experience in Julia
Graph Theory Basics
Neural Networks Fundamentals
|
|
|
Contact
|
M.Sc. Nicolas Hornek,
room 1.402 (ETI II),
phone 685-67992, [E-Mail]
Dipl.-Ing. Filippos Christou,
room 1.319 (ETI II),
phone 685-67968, [E-Mail]
|
|
|
|
|
|
|