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Institute of Communication Networks and Computer Engineering (IKR)

Project description


Master thesis No. 1050    (Offer)   [pdf]

Network Traffic Prediction with Machine Learning using Gaussian Processes



Machine learning
Gaussian processes
Bayesian statistics

Traffic modeling
Traffic prediction




In today's demanding networking landscape, network operators face the dual challenge of upholding stringent QoS (Quality of Service) standards while managing costs effectively. Balancing OpEx (Operational Expenses) reduction with efficient equipment utilization is crucial, and accurate traffic predictions play a pivotal role in achieving this optimization. The ability to anticipate future traffic patterns empowers network operators to design and implement strategies that maximize network utilization and minimize unnecessary investments in new hardware.

Gaussian Processes (GPs) have emerged as a prominent ML (Machine Learning) technique, employing multivariate normal distributions to effectively model stochastic processes under the Bayesian paradigm. Their remarkable ability to quantify uncertainty across a wide range of disciplines has cemented their position as a powerful tool.

Problem Description

In the context of this thesis, you are called to use GPs to model network traffic demands. More specifically, the thesis consists of the following steps:

getting familiar with GPs and related Julia packages

investigation of network traffic patterns

implementing GPs for modeling the backbone network traffic

evaluation of the models

Acquired Knowledge and Skills

In this thesis, you will enrich your knowledge of GPs and probabilistic inference, a methodology gaining growing focus. You will also experiment with the scientific programming language Julia. Finally, you will get a great insight into networking and network services.


Desirable knowledge

Communication Networks Architecture and Design
Programming Experience

Programming Experience in Julia


Dipl.-Ing. Filippos Christou, room 1.319 (ETI II), phone 685-67968, [E-Mail]