Fog-enabled Industrial IoT Network Slicing model based on ML-enabled Multi-objective Optimization
| Autor: | Sami Tabbane, Amel Ksentini, Maha Jebalia |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Edge device
Computer science business.industry Distributed computing Quality of service 020206 networking & telecommunications Cloud computing 02 engineering and technology computer.software_genre Slicing Multi-objective optimization Middleware (distributed applications) 0202 electrical engineering electronic engineering information engineering Workbench 020201 artificial intelligence & image processing business computer Reference model |
| Zdroj: | WETICE |
| DOI: | 10.1109/wetice49692.2020.00042 |
| Popis: | Fog Computing, as a distributed middleware layer, is expected to bring Cloud capabilities closer to the IoT edge devices. Industrial IoT systems may benefit from the geographically distributed features of the fog nodes, to enhance several QoS requirements such as delay. However, heterogeneous IIoT data traffics require a specific process for each type of them. Thus we refer to a priority classification scheme to slice a fog-enabled IIoT network. For this purpose, we perform a multi-objective optimization algorithm enabled by a machine learning workbench to set a slicing strategy, taking into account the specific and relevant QoS metrics of each priority class. Our slicing model performs better results, in terms of data-rate, e2e delay and network usage, compared to a non-sliced reference model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|