Abschlussarbeiten

In dieser Arbeit werden Methoden untersucht, um Ansätze aus dem Bereich Deep Reinforcement Learning auf (Multi-Agent) Environments mit dynamischen (sich ändernden) Graphen anwenden zu können. Dies ist ein wichtiger Schritt zur Anwendung in realen Kommunikationsnetzwerken. Existierende Arbeiten sind hinsichtlich der möglichen Anpassungen im Graphen oftmals sehr beschränkt und/oder müssen bei starken Änderungen im Graphen neu trainiert werden.

Die Data Plane eines 5G Zugangsnetzwerkes besteht aus mehreren Netzfunktionen, die über dedizierte Interfaces verbunden sind. Ziel dieser Arbeit ist es eine Leistungsanalyse entlang der Data Plane Netzfunktionen durchzuführen und zu evaluieren, welchen Einfluss einzelne Netzfunktionen auf die Ende-zu-Ende Performanz haben. Zusätzlich soll evaluiert werden, an welchen Stellen entlang der Data Plane welche Leistungsbeschränkungen existieren und mit welchen Mechanismen man die erkannten Verluste minimieren kann. Anschließend sollen diese Mechanismen in ausgewählten Netzfunktionen im Testnetzwerk des FG KOM implementiert werden und die Auswirkungen evaluiert werden.

The overarching goal of this bachelor's thesis is to find out if approaches from the domain of learnable communication can enable collaborative perception in the Pommerman domain. Existing approaches for the team radio mode with partial observability employ heuristics to exchange information between agents in order to increase their win rate. As the communication bandwidth is very limited (only 6 bits per timestep), manually designing a sophisticated communication protocol is complex. Consequently, existing heuristics are kept simple and usually convey very litte information (e.g. own position and position of one opponent). However, both agents have access to further local information that could be worth sharing (e.g. powerup locations, map tiles, bomb locations and movement..). This thesis investigates if approaches with learnable communication could be used to create communication protocols that allow to convey more information under the same bandwidth constraints.

To deal with rapidly changing workloads,  Distributed Stream Processing Systems rely on parallelism to use multiple instances of operators to process a high amount of data. In recent times, several heuristic-based and learned-based approaches have been proposed to tune parallelism, but they are limited to specific workloads and can't work for unseen workloads which could result in degraded performance and inefficient resource utilization. In this thesis, we look at how can end-to-end learning model be  used for parallelism prediction in distributed systems.  The proposed model should aim to accurately predict optimal parallelism levels under a variety of scenarios, thus improving data processing efficiency and resource utilization in distributed systems. 

Graph Neural Networks (GNNs) have shown potential in various domains, including databases and distributed stream processing systems. However, the effectiveness and accuracy of GNN models, particularly in zero-shot learning scenarios, heavily depend on selecting appropriate features. This thesis aims to develop and evaluate feature selection strategies and an optimized method to select from feature selection strategies based on constraints that enhance the performance of zero-shot-based GNN models in the context of parallel stream processing.

Key Objectives

• Identify and analyze the key features that impact the performance of GNN models in parallel stream processing tasks.
• Develop a systematic feature selection strategy that optimizes the zero-shot learning capabilities of GNN models in distributed stream processing environments.
• Evaluate the effectiveness of the proposed feature selection strategy in improving the accuracy, efficiency, and scalability of zero-shot GNN models.

In recent years, there has been an increasing interest in adopting federated learning to train deep learning models. 
This heightened interest is primarily driven by the agility, data privacy, scalability, and efficiency that federated learning has demonstrated.
Federated learning (FL) has gained significant traction, particularly in developing and updating object detection models.
These models demand continuous refinement, a task that FL can efficiently accomplish. Nonetheless, a critical challenge for these models revolves around the collection and preparation of training data.
This issue is particularly pronounced in vehicular networks, where each vehicle or node must gather images and prepare them for their respective local models.
However, a pivotal prerequisite is labeling these images before they can be utilized in training supervised models.
This thesis presents several methodologies for image labeling, offering an in-depth exploration of each approach, and showing their respective advantages and drawbacks (e.g., in the model performance, required computational power, and network load).
Furthermore, it conducts a comprehensive assessment of the performance and accuracy of these approaches, utilizing a variety of metrics for evaluation.

Task

• Explore federated learning applications, with a particular focus on their utilization within the domain of vehicular applications. 
• Extend the implementation of the pre-existing framework for training federated learning-based object detection models. This extension should encompass the integration of diverse data labeling methodologies.
• Conduct a thorough and exhaustive evaluation of the performance of these aforementioned approaches. This assessment should encompass the utilization of a diverse array of metrics for evaluation purposes, ensuring a comprehensive understanding of their respective strengths and weaknesses.
• Report the findings

Prerequisites

It is required to have:
•    Motivated and individual working style
•    Very good programming skills in Python.
•    Very good machine learning / Deep learning knowledge

Contact

I am looking for motivated students interested in working on cutting-edge technologies and vehicular applications area. If you are interested in writing a Bachelor's or Master's Thesis, please feel free to contact me at Ahmad Khalil (ahmad.khalil(at)kom.tu-darmstadt.de). You can email me the following information:
•    Your CV or small text about your courses and prior experiences in this area.

Das am Fachgebiet KOM entwickelte OpenSource Framework P4STA (https://github.com/ralfkundel/P4STA) erlaubt hochgenaue Latenzmessungen mit Nanosekundengenauigkeit bei Bandbreiten von bis zu 100 Gbit/s. Um dies zu erreichen werden softwarebasierte Standardlastgeneratoren mit einem programmierbaren Netzwerkswitch kombiniert.

Nachteil hierbei ist, dass die Softwarelastgeneratoren zusätzliche Server benötigen und nur eingeschränkt flexibel sind. Spezielle Netzwerkprotokolle wie beispielsweise PPPoE oder GTP, was in 5G Netzwerken zum Einsatz kommt, können nur schwierig erzeugt werden.

Ziel dieser Arbeit ist es, dass P4STA framework um integrierte Paketgenerierung innerhalb des programmierbaren P4-Switches zu erweitern.
Der Vorteil dieser hardwarebasierten Lastgenerierung liegt zum einen in den hohen Datenraten; pro Port können so bis zu 100 Gbit/s erzeugt werden.
Außerdem können beliebige Protokollstacks erzeugt werden.

Voraussetzungen:

- Gutes Verständnis von Netzwerken und Protokollen
- Vorlesung Software Defined Networking (oder vergleichbar)
- idealerweise Grundkenntnisse in der P4 oder FPGA Entwicklung
- Erfahrung in der Softwareentwicklung, idealerweise z.B. Python/Django/HTML/Javascript

Forschungsfragen, die im Rahmen dieser Arbeit beantwortet werden:

• wie flexibel kann mit hardwarebasierten Lastgeneratoren Last erzeugt werden?
• wird durch eine integrierte Lasterzeugung die Genauigkeit beeinflusst?
• ... weitere Fragestellungen, die im Rahmen der Bearbeitung herausgearbeitet werden ...

Problem Description

We maintain a crawler for libp2p-based Kademlia DHTs, which we use to crawl IPFS.
We are able to enumerate all DHT servers, i.e., all nodes participating as server nodes in the DHT.
We can then visualize data about these nodes, such as agent versions, churn, geolocation, etc.

The crawler, however, is generic over all DHTs implemented using go-libp2p-kad-dht.
There are many projects using this implementation, which we could potentially crawl.
They differ in a) their bootstrap nodes, and b) their protocol identifiers.

Main Objective

Working on well-connected Linux servers, set up multiple instances of the crawler, enumerating peers of as many networks as possible.
Set up automated data wrangling and visualization pipelines, displaying the results on a statically-generated website.

Optional Bonus Goals

• Extend the visualization and data science scripts.
• Realize the setup in a containerized environment, in a way that is easily extensible with new networks.
• Save the results of the crawls into a PostgreSQL database for compact storage and easier queries.
• ???

Prerequisites

You must

• Have intrinsic motivation to look at P2P networks.
• Be able to fluently work on remote Linux machines via SSH.
• Understand Go code.
• Be able to admin Linux machines and document the setup.

It would probably be helpful if

• You understood how DHT crawlers work in general.
• You knew Chinese, since many of the projects have Chinese source code comments or documentation.
• You knew R, since the evaluation scripts are in R.
• You were well-versed in SQL and design of database schemas.

Bachelor/Master?

If you pursue this as a Bachelor's thesis, the minimum goal and any number of optional goals apply.

If you pursue this as a Master's thesis, you'll have to significantly extend the data processing and visualization frameworks to derive new interesting data from the datasets. Addiitonally, you'll of course have to implement the setup in a clean way, well-documented, with more of the optional goals realized. I'd also expect you to come up with your own optional goals.

Motivation

Object detection is a crucial task in the field of autonomous vehicles. To train the object detection models, data such as RGB data is collected either offline or online during the operation of the cars, and this data is used to train the models. However, in supervised learning approaches, which have superior performance, the data needs to be annotated.

Active learning is an approach that aims to reduce the amount of annotated data required. It achieves this by selecting the most informative information from the unannotated data pool. Unfortunately, most active learning approaches for object detection assume that the data is already gathered and located in a central entity (offline). These approaches overlook the consumption of networking resources that occur when transmitting the data in the online data gethering.

In this thesis, the student will have the chance to enable online data collection in a multi-agent system (e.g., vehicular system), while considering the available network resources. The objective is to develop an active learning approach that optimizes the utilization of the network resources while maintaining high object detection performance. This would involve selecting the most informative data for annotation and training, taking into account the limitations of the available network resources.

The goal is to enhance the efficiency and effectiveness of object detection in autonomous vehicles by developing an active learning approach that leverages the available network resources without compromising the performance of the models.

Task

This thesis is open for master/bachelor students, and the main tasks are as follows:
•    Explore relevant existing related literature.
•    Formalizing the problem and modeling the solution and
•    Setting up a development environment.
•    Evaluation of the proposed solution in comparison to the existing state-of-the-art approaches.

Prerequisites

It is required to have:
•    Motivated and individual working style
•    Very good programming skills in Python
•    Very good machine learning / Deep learning knowledge

Related Literature

[1] Yu, Weiping, et al. "Consistency-based active learning for object detection." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.

[2] Choi, Jiwoong, et al. "Active learning for deep object detection via probabilistic modeling." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.

[3] Feng, Di, et al. "A review and comparative study on probabilistic object detection in autonomous driving." IEEE Transactions on Intelligent Transportation Systems 23.8 (2021): 9961-9980.

Motivation 

Object detection holds paramount importance in vehicular applications, serving as a cornerstone for road safety, efficient traffic management, and the evolution of autonomous driving systems. However, while training the object detection models in vehicular applications, the intricacies of data privacy, constrained communication bandwidth, and computational limitations present significant challenges. This thesis aims to leverage the potential of adaptive model compression techniques to augment Federated Learning-based object detection model training in vehicular applications. The central premise is to develop compression strategies (such as pruning, and quantization) that dynamically respond to contextual information, such as weather conditions, traffic density, and time of day, to optimize model training efficiency and accuracy. This integration holds the potential to create more resource-efficient model training that can adapt to changing environments and perform optimally under varying conditions.

Task

This thesis is open for master's students, and the main tasks are as follows:
•    Explore relevant existing related literature.
•    Formalizing the problem and modeling the solution and
•    Setting up a development environment and implementing the proof of concept or prototype.
•    Evaluation of the proposed solution in comparison to the existing state-of-the-art approaches.

Prerequisites

It is required to have:
•    Motivated and individual working style
•    Very good programming skills in Python.
•    Very good machine learning / Deep learning knowledge

Contact

I am looking for motivated students interested in working on cutting-edge technologies and vehicular applications area. If you are interested in writing a Bachelor's or Master's Thesis, please feel free to contact me at Ahmad Khalil (ahmad.khalil(at)kom.tu-darmstadt.de). You can email me the following information:
•    Your CV or small text about your courses and prior experiences in this area.

Motivation 

As the development of autonomous vehicles continues to progress, the need for improving detection models becomes increasingly important. However, sharing the data gathered from vehicles with a central server can raise privacy concerns and consume a significant amount of network capacity. Federated learning provides a solution to this challenge by allowing for the training of machine learning models on decentralized data, without the need to transfer raw data to a central server. However, federated learning inherits convergence issues, especially when the data is non-iid (non-independent and identically distributed). This makes studying how the data distribution influences the performance of perception models trained in a federated learning setup in the vehicular collective perception field crucial. By analyzing state-of-the-art approaches and developing novel methods that take into account the data heterogeneity, the student will help improving the accuracy and reliability of perception models. Moreover, training models locally in the vehicle poses significant challenges that require careful consideration, such as dealing with the labeling issue of data collected in real-time. The student will have the opportunity to address these challenges and contribute to the future of autonomous vehicles.

Task

This thesis is open for master's students, and the main tasks are as follows:
•    Explore relevant existing related literature.
•    Formalizing the problem and modeling the solution and
•    Setting up a development environment and implementing the proof of concept or prototype.
•    Evaluation of the proposed solution in comparison to the existing state-of-the-art approaches.

Prerequisites

It is required to have:
•    Motivated and individual working style
•    Very good programming skills in Python.
•    Very good machine learning / Deep learning knowledge

Contact

I am looking for motivated students interested in working on cutting-edge technologies and vehicular applications area. If you are interested in writing a Bachelor's or Master's Thesis or gaining experience in MAKI project as HiWi, please feel free to contact me at Ahmad Khalil (ahmad.khalil(at)kom.tu-darmstadt.de). You can email me the following information:
•    Your CV or small text about your courses and prior experiences in this area.