@techreport{DworzakGrossmannLe2023,
  type      = {Working Paper},
  author    = {Dworzak, Manuel and Großmann, Marcel and Le, Duy Thanh},
  title     = {Federated Learning for Service Placement in Fog and Edge Computing},
  series = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  journal   = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  doi       = {10.25972/OPUS-32219},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-322193},
  pages     = {4},
  year      = {2023},
  abstract  = {Service orchestration requires enormous attention and is a struggle nowadays. Of course, virtualization provides a base level of abstraction for services to be deployable on a lot of infrastructures. With container virtualization, the trend to migrate applications to a micro-services level in order to be executable in Fog and Edge Computing environments increases manageability and maintenance efforts rapidly. Similarly, network virtualization adds effort to calibrate IP flows for Software-Defined Networks and eventually route it by means of Network Function Virtualization. Nevertheless, there are concepts like MAPE-K to support micro-service distribution in next-generation cloud and network environments. We want to explore, how a service distribution can be improved by adopting machine learning concepts for infrastructure or service changes. Therefore, we show how federated machine learning is integrated into a cloud-to-fog-continuum without burdening single nodes.},
  language  = {en}
}