@phdthesis{Geissler2022,
  author    = {Geißler, Stefan},
  title     = {Performance Evaluation of Next-Generation Data Plane Architectures and their Components},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-26015},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-260157},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {In this doctoral thesis we cover the performance evaluation of next generation data plane architectures, comprised of complex software as well as programmable hardware components that allow fine granular configuration. In the scope of the thesis we propose mechanisms to monitor the performance of singular components and model key performance indicators of software based packet processing solutions. We present novel approaches towards network abstraction that allow the integration of heterogeneous data plane technologies into a singular network while maintaining total transparency between control and data plane. Finally, we investigate a full, complex system consisting of multiple software-based solutions and perform a detailed performance analysis. We employ simulative approaches to investigate overload control mechanisms that allow efficient operation under adversary conditions. The contributions of this work build the foundation for future research in the areas of network softwarization and network function virtualization.},
  subject      = {Leistungsbewertung},
  language  = {en}
}
@article{WamserSeufertHalletal.2021,
  author    = {Wamser, Florian and Seufert, Anika and Hall, Andrew and Wunderer, Stefan and Hoßfeld, Tobias},
  title     = {Valid statements by the crowd: statistical measures for precision in crowdsourced mobile measurements},
  series = {Network},
  volume    = {1},
  journal   = {Network},
  number    = {2},
  issn      = {2673-8732},
  doi       = {10.3390/network1020013},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-284154},
  pages     = {215 -- 232},
  year      = {2021},
  abstract  = {Crowdsourced network measurements (CNMs) are becoming increasingly popular as they assess the performance of a mobile network from the end user's perspective on a large scale. Here, network measurements are performed directly on the end-users' devices, thus taking advantage of the real-world conditions end-users encounter. However, this type of uncontrolled measurement raises questions about its validity and reliability. The problem lies in the nature of this type of data collection. In CNMs, mobile network subscribers are involved to a large extent in the measurement process, and collect data themselves for the operator. The collection of data on user devices in arbitrary locations and at uncontrolled times requires means to ensure validity and reliability. To address this issue, our paper defines concepts and guidelines for analyzing the precision of CNMs; specifically, the number of measurements required to make valid statements. In addition to the formal definition of the aspect, we illustrate the problem and use an extensive sample data set to show possible assessment approaches. This data set consists of more than 20.4 million crowdsourced mobile measurements from across France, measured by a commercial data provider.},
  language  = {en}
}