TY  - RPRT
A1  - Simon, Manuel
A1  - Gallenmüller, Sebastian
A1  - Carle, Georg
T1  - Never Miss Twice - Add-On-Miss Table Updates in Software Data Planes
T2  - KuVS Fachgespräch - Würzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS’23)
N2  - State Management at line rate is crucial for critical applications in next-generation networks. P4 is a language used in software-defined networking to program the data plane. The data plane can profit in many circumstances when it is allowed to manage its state without any detour over a controller. This work is based on a previous study by investigating the potential and performance of add-on-miss insertions of state by the data plane. The state keeping capabilities of P4 are limited regarding the amount of data and the update frequency. We follow the tentative specification of an upcoming portable-NIC-architecture and implement these changes into the software P4 target T4P4S. We show that insertions are possible with only a slight overhead compared to lookups and evaluate the influence of the rate of insertions on their latency.
KW  - SDN
KW  - state management
KW  - P4
KW  - Add-on-Miss
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-322071
ER  - 
TY  - RPRT
A1  - Dworzak, Manuel
A1  - Großmann, Marcel
A1  - Le, Duy Thanh
T1  - Federated Learning for Service Placement in Fog and Edge Computing
T2  - KuVS Fachgespräch - Würzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS’23)
N2  - 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.
KW  - fog computing
KW  - SDN
KW  - orchestration
KW  - federated learning
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-322193
ER  -