Praktika

• 1.

  Amir, M.: Simulative Performance Evaluation of Short Video Services in Shared Access Networks, (2024).

• 1.

  Leidinger, F.: Measuring the Effectiveness of Latency Mitigation Methods with a Prototype Video Game, (2022).
  • [ Abstract ]
  Video games with a competitive multiplayer modes must deal with the troubles of high and varying end-to-end lag. They can employ varying methods to manage, mitigate or conceal this lag and equalize it between different clients. But the closed source nature of video games makes examining the effectiveness of these mechanisms hard. Your job is to implement a simple video game prototype from scratch that uses an authoritative client-server multiplayer approach and implements such lag mitigation mechanisms. With this prototype we want to get a better understanding of the effectiveness of these lag concealment measures.
• 1.

  Sichermann, M.: Building a GAN from Scratch for Synthesizing Data Samples for the Use Case of Browser Fingerprinting, (2022).
  • [ Abstract ]
  With the recent rise of Machine Learning and especially Deep Learning, gathering enough data is a challenging task. Generative Adversarial Networks (GANs) have emerged to synthesize fake data from real data, by employing two neural networks that interact with each other, i.e., a discriminator and a generator, where the generator tries to trick the discriminator into classifying a fake sample as real. GANs are usually employed on images, however, this has recently been adopted in other research areas. This thesis investigates the capability of GANs to generate fake samples of network traffic, e.g., for data augmentation to balance out datasets (for intrusion detection, for example).

• 1.

  Seybold, J.: Networking models and lag prevention methods of old games, (2021).

• 1.

  Spangenberger, M.: Implementation and Evaluation of Content Encoding at Runtime for DASH Video Streaming, (2020).
• 1.

  Schüler, J.: Flow Statistics Calculation in a Programmable Data Plane with P4, (2020).
• 1.

  Vomhoff, V.: Reliability of Crowdsourced Measurements, (2020).
• 1.

  Hildebrand, K.: Impact of Rendering Times on the Perceived Loading Speed of Web Pages, (2020).
  • [ Abstract ]
  Contact/Kontakt: michael.seufert@uni-wuerzburg.de Title: Impact of Rendering Times on the Perceived Loading Speed of Web Pages Abstract: The most dominant QoE (Quality of Experience, i.e., subjectively perceived quality) influence factor of web browsing is the page load time. However, pages do not load in a single event, but different parts of the web page are displayed at different rendering times. In this work, a study shall be conducted to investigate the impact of rendering times on the perceived loading speed of web pages. The results of the study shall be evaluated to derive recommendations for networking as well as rendering aspects of web browsing. Titel: Einfluss von Anzeigezeiten auf die wahrgenomme Ladegeschwindigkeit von Webseiten Inhalt: Der wichtigste Einflussfaktor auf die QoE (Quality of Experience, d.h. subjektiv empfundene Dienstgüte) beim Webbrowsing ist die Seitenladezeit. Jedoch laden Webseiten nicht auf einmal, sondern verschiedene Teile der Seite werden zu unterschiedlichen Zeitpunkten angezeigt. In dieser Arbeit soll eine Studie durchgeführt werden, mit der der Einfluss dieser Anzeigezeiten auf die wahrgenommene Ladegeschwindigkeit untersucht werden kann. Die Ergebnisse der Studie sollen ausgewehrtet werden, um Empfehlungen für Netzwerk- und Renderingaspekte beim Webbrowsen abzuleiten.
• 1.

  Raffeck, S.: Towards Modeling Energy Performance of LoRaWAN Transmissions: A Verification of Theoretical Predictions, (2020).

• 1.

  Tönsing, L.: Untersuchung des Einflusses der Anleitungs- und Bearbeitungssprache auf die Ergebnisqualität von Crowdsourcing-Aufgaben, (2019).
• 1.

  Dietz, K.: Extending the OpenFlow OMNeT++ Suite, (2019).
• 1.

  Smietanka, O.: Comparison of LPWAN IoT Device Communication Techniques LoRa and NB-IoT, (2019).
• 1.

  Biskup, F.: IoT-Datenanalyse im Smart City-Kontext, (2019).
• 1.

  Stulier, N.: Statistische Evaluation der APM in DOTA 2, (2019).
• 1.

  Janiak, L.: Analysis of Geographic Data in R, (2019).

• 1.

  Steurer, F.: Optimization of Scheduling Mechanisms for Crowdsourcing Tasks Considering Time-dependent System Parameters, (2018).
• 1.

  Popp, C.: Analysis of WebRTC Video Communications in a Cloud Testbed Setup, (2018).

• 1.

  Wehner, N.: Unsupervised QoE Field Study for Mobile YouTube Video Streaming with YoMoApp, (2017).
• 1.

  Kamneng Kwam, B.: Static Placement of Services on Edge Servers, (2017).
• 1.

  Sommer, M.: Implementation of an OpenFlow Fuzzing Framework, (2017).
• 1.

  Knapp, T.: Extending the Monitoring Features of ONOS, (2017).
  • [ Abstract ]
  The SDN controller ONOS lacks certain network monitoring capabilities. The task is to develop an ONOS application which utilizes the interfaces provided by ONOS and enableds the active measurement of a predefined set of network metrics.
• 1.

  Ott, M.: Extraction of SDN Device Fingerprints for Enhancing the Network Security, (2017).

• 1.

  Reinhart, L.: An SDN-based Implementation of a Network-controlled “Bring Your Own Device” Scenario, (2016).
• 1.

  Schwind, A.: WhatsAnalyzer: a Tool for Collecting and Analyzing WhatsApp Chats, (2016).
• 1.

  Beutel, A.: Correlation of Subjective and Objective Measurements in Enterprise Networks, (2016).
• 1.

  Werthmann, A.: Untersuchung der Sicherheit von Desktop- und Smartphone-Applikationen hinsichtlich HTTPS-Man-in-the-middle-Angrifen, (2016).
• 1.

  Schreck, M.: Matlab REST API, (2016).
• 1.

  Pfaff, B.: A Behavior-driven Approach for Testing Softwarized Networks using OpenStack Infrastructures, (2016).
• 1.

  Schmitt, N.: Implementation of a Video Quality Reference Metric for H264 using ffmpeg, (2016).
• 1.

  Züfle, M.: Testbed Validation and Comparison with Simulation, (2016).
  • [ Abstract ]
  In this report the main components of a testbed for evaluating the performance of resource management strategies has been validated. Application-aware resource management is a very important task since high quality applications like video streaming, social networking, and online gaming need a more intelligent bandwidth allocation policy than best effort to reach the highest possible Quality of Experience for all end users. Several tests have been performed to validate the determination of the web page size, Emma, Dory, and TraSh. It has been shown that the web page size has to be determined by measuring the used bandwidth at the router using a tool like ifstat since HTTP and TCP headers are not considered by tools within the browser like Firebug. Additionally, measurements provided that the latency caused by sending the resource allocation commands from Dory to TraSh is very small and should not affect the resource management results. Furthermore, the different random number generator distributions have been examined. It has been shown that Emma typically generates too many request arrivals with too low interarrival time so that requests are rejected. The validation of TraSh has shown that the time needed to realize the resource allocation adaption is pretty short and that there is no statistically significant difference between this delay for different capacities. On top of that, several problems of TraSh have been detected. Finally, the download times of web browsing requests have been measured and compared to simulation results. It has been shown that the download times in the measurement are much higher than the ones of the simulation if there is no initial delay implemented. By implementing an initial delay in the simulation it was shown that the download times converge. However, the initial delay needs to be further investigated since the elapsed time is not reliable known. On top of that, the problems of TraSh need more examination and resolving. Finally, the resource management strategies need to be implemented in Dory in order to measure their impact on download times for web browsing and buffering ratio for video streaming.
• 1.

  Ott, S.: Browser-based Measurement of HTTP Live Streaming Performance during the Olympic Games 2016, (2016).
• 1.

  König, F.: Cloud Computing with OpenStack: Deployment and Networking, (2016).

• 1.

  Müssig, A.: Comparison of the Data Plane Performance of a Commercial Hardware Firewall and its Software Pendant, (2015).
• 1.

  Geissler, S.: Elephant Detection using SDN, (2015).
• 1.

  Kuhn, N.: Analysis of the co-working network of a commercial crowdsourcing plattform, (2015).
• 1.

  Allendorf, F.: Assessing the Accuracy of the NetGraph Network Measurement Tool in Dota 2, (2015).

• 1.

  Scherer, J.: SysStew: ein Framework zur Test-Automatisierung, (2014).
• 1.

  Ehrmann, K.: Application and Network Monitoring of HLS Live Streaming of the 2014 FIFA World Cup, (2014).
• 1.

  Rachor, C.: Emulation of Real-World Topologies with OFCProbe, (2014).

• 1.

  Lange, S.: Graph Sampling in Online Social Networks, (2013).

• 1.

  Amrehn, P.: Crowdsourcing based Network Measurements using the Example of YouTube, (2012).
• 1.

  Metter, C.: An Evaluation of Open-Source Cloud Platforms and Analysis of VM-Migration Traffic, (2012).

• 1.

  Mylek, J.: Implementation of a Vuze Plugin, (2010).
• 1.

  Rüger, M.: Overlay-Connection Usage in BitTorrent und Tribler, (2010).

• 1.

  Hock, D.: QoE and QoS - A natural relationship?, (2007).

• 1.

  Weiß, M.: Modified Earliest Deadline First: A Performance Study, (2006).

• 1.

  Oberdalhoff, H.-C.: Resilient Multi-Topology Routing: Design and Performance Evaluation, (2004).
• 1.

  Emmert, B.: Effects of Soft Handover and Outer Loop Power Control in UMTS Networks, (2004).
• 1.

  Oechsner, S.: Measurement Based Admission Control: A Simulation Study, (2004).

• 1.

  Kopf, S.: Design and Performance Evaluation of Budget Based Network Admission Control Methods in Differentiated Services IP Networks, (2003).
• 1.

  Gehrsitz, S.: Calculation of Network Admission Control Budgets, (2003).
• 1.

  Wagner, B.: A simulativ study of WAP traffic, (2003).

• 1.

  Reifert, A.: Design and Investigation of a Distributed Constraint- Based Routing Algorithm for Real-Time Networks, (2002).
• 1.

  Mäder, A., Brotzeller, M.: Investigation and Optimization of IP Routing Protocols, (2002).
• 1.

  Holzinger, F.: A tool for displaying factory informations, (2002).

• 1.

  Martin, R.: Assuring End-to-End Quality of Service: Concepts for Resource Reservation Setup Protocols - Description and Analysis, (2001).
• 1.

  Binzenhöfer, A., Noack, P.: Simuation of queuing mechanisms for IP networks, (2001).
• 1.

  Eckhardt, F.: Scalability in Quality of Service IP Networks, (2001).

• 1.

  Hauck, N.: Scalability in Quality of Service networks using MPLS, (2000).
• 1.

  Hoßfeld, T.: Measurement and Analysis of Internet Traffic and TCP-Multiplex, (2000).

• 1.

  Eglmaier, D.: Simulativer Vergleich verschiedener Zugriffsmechanismen für Metropolitan Area Networks, (1992).

• 1.

  Lagemann, C.: A Markov chain model for TCP flows, (1000).
• 1.

  Ermolaev, P.: A tool to simulate TCP connections, (1000).