@techreport{GrossmannLe2023,
  type      = {Working Paper},
  author    = {Großmann, Marcel and Le, Duy Thanh},
  title     = {Visualization of Network Emulation Enabled by Kathar{\´a}},
  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-32218},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-322189},
  pages     = {4},
  year      = {2023},
  abstract  = {In network research, reproducibility of experiments is not always easy to achieve. Infrastructures are cumbersome to set up or are not available due to vendor-specific devices. Emulators try to overcome those issues to a given extent and are available in different service models. Unfortunately, the usability of emulators requires time-consuming efforts and a deep understanding of their functionality. At first, we analyze to which extent currently available open-source emulators support network configurations and how user-friendly they are. With these insights, we describe, how an ease-to-use emulator is implemented and may run as a Network Emulator as a Service (NEaaS). Therefore, virtualization plays a major role in order to deploy a NEaaS based on Kathar{\´a}.},
  language  = {en}
}
@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}
}
@techreport{LeGrossmannKrieger2022,
  type      = {Working Paper},
  author    = {Le, Duy Thanh and Großmann, Marcel and Krieger, Udo R.},
  title     = {Cloudless Resource Monitoring in a Fog Computing System Enabled by an SDN/NFV Infrastructure},
  series = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)},
  journal   = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)},
  doi       = {10.25972/OPUS-28072},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-280723},
  pages     = {4},
  year      = {2022},
  abstract  = {Today's advanced Internet-of-Things applications raise technical challenges on cloud, edge, and fog computing. The design of an efficient, virtualized, context-aware, self-configuring orchestration system of a fog computing system constitutes a major development effort within this very innovative area of research. In this paper we describe the architecture and relevant implementation aspects of a cloudless resource monitoring system interworking with an SDN/NFV infrastructure. It realizes the basic monitoring component of the fundamental MAPE-K principles employed in autonomic computing. Here we present the hierarchical layering and functionality within the underlying fog nodes to generate a working prototype of an intelligent, self-managed orchestrator for advanced IoT applications and services. The latter system has the capability to monitor automatically various performance aspects of the resource allocation among multiple hosts of a fog computing system interconnected by SDN.},
  subject      = {Datennetz},
  language  = {en}
}