Open Thesis

Dynamic network planning for the future railway communications

Keywords:
Network Planning, On-Train Data Communications
Short Description:
Exploration of mechanisms for handling data communications under the influence of mobility in the German long distance railway system.

Description

This work focuses on the exploration of networks enabling train control and on-board data communications under mobility scenarios. Today, low bandwidth networks such as GSM, providing less than 200 Kbps are being used to transmit train control information. Moreover, despite trains may use multiple on-board technologies to provide users with an internet connection (e.g., repeaters, access points), they fail in their attempt as these connections are characterized by having low throughput (less than 2 Mbps) and frequent service interruptions.

This work aims at the development of a network planning solution enabling future applications in train mobility scenarios such as: Automatic Train Operation (ATO) [1,2], leveraging cloud technologies and meeting bandwidth requirements of data-hungry end-users' applications. Here, special attention will be given to the migration of communications services triggered by trains mobility patterns. It is expected of the student to find solutions to the following questions:

  • When to trigger service migrations?

  • Where to migrate services? (i.e., to which data center)

  • How to handle this process? (So that the user does not perceive any interruption)

 Given:

  • Trains mobility patterns

  • Service requirements in terms of bandwidth and delay

  • Network topology

  • Data center locations

 
The results from this work can be useful to get an insight on requirements for Smart Transportation Systems, that may in turn be useful for cementing the basis of other scenarios such as: Autonomous Driving and Tele-Operated Driving.

 [1] Digitale Schiene Deutschland. Last visit on 13.12.2021 https://digitale-schiene-deutschland.de/FRMCS-5G-Datenkommunikation

[2] 5G-Rail FRMCS. Last visit on 13.12.2021 https://5grail.eu/frmcs/

Prerequisites

Basic knowledge in:

  • Integer Linear Programming (ILP), heuristics or Machine Learning (ML).

  • Python

Please send your CV and transcript of records.

 

Contact

Supervisor:

Cristian Bermudez Serna

Data plane performance measurements

Keywords:
P4, SDN
Short Description:
This work consists on performing measurements for a given P4 code on different devices.

Description

Software-Defined Networking (SDN) is a network paradigm where control and data planes are decoupled. The control plane consists on a controller, which manages network functionality and can be deployed in one or multiple servers. The data plane consists on forwarding devices which are instructed by the controller on how to forward traffic.

P4 is a domain-specific programming language, which can be used to define the functionality of forwarding devices as virtual or hardware switches and SmartNICs.

This work consists on performing measurements for a given P4 code on different devices. For that, an small P4-enabled virtual network will be used to perform some measurments. Later, data will be also collected from hardware devices as switchs and SmartNICs. Measurement should be depicted in a GUI for its subsequent analysis.

Prerequisites

Basic knowledge on the following:

  • Linux
  • Networking/SDN
  • Python/C
  • Web programming (GUI).

Please send your CV and transcript of records.

Contact

Supervisor:

Cristian Bermudez Serna

Multi-domain network implementation

Keywords:
multi-domain, SDN
Short Description:
This works consists on the implementation of a multi-domain SDN network.

Description

 Software-Defined Networking (SDN) is a network paradigm where control and data planes are decoupled. The control plane consists on a controller, which manages network functionality and can be deployed in one or multiple servers. The data plane consists on forwarding entities which are instructed by the controller on how to forward traffic.

 A network can be divided in multiple domains in order to ease its management or limit ownership. In multi-domain SDN, each domain has a controller which is responsible for the management. Controllers in different domains cooperate which each other aiming at providing multi-domain end-to-end connectivity.

 In this work, the student will receive an abstract topology representing the multi-domain network. This information has to be used to build a virtual network, that can be used in the testing of different algorithms. The implementation should include a GUI, in order to visualize the topology and interact with the different elements in the network.

Please send your CV and transcript of records.

Prerequisites

Basic knowledge on the following:

  • Linux
  • Networking/SDN
  • Python
  • Web programming (GUI)

Contact

Supervisor:

Cristian Bermudez Serna

Machine learning for in-network prediction and classification

Keywords:
Machine Learning (ML), Artificial Intelligence (AI), Software Defined Networking (SDN)

Description

In this work, Machine Learning (ML) alternatives will be evaluated to perform in-network prediction and classification. The goal is to develop ML-based applications that help in network optimization, reconfiguration, telemetry and resource allocation.

Some of the tasks include:

  • Dataset creation
  • ML algorithm implementation
  • Evaluation in terms of accuracy and resource utilization

If you are interested, please send me your CV and your transcript of records

Prerequisites

Knowledge in the following topics:

  • Computer networking
  • Software Defined Networking (SDN)
  • Machine learning 
  • Programming languages (Python)
  • Linux

Supervisor:

Cristian Bermudez Serna