A framework for the joint placement of edge service infrastructure and User Plane Functions for 5G
Achieving less than 1 ms end-to-end communication latency, required for certain 5G services and use cases, is imposing severe technical challenges for the deployment of next-generation networks. To achieve such an ambitious goal, the service infrastructure and User Plane Function (UPF) placement at...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/175071 |
| Acceso en línea: | https://hdl.handle.net/2117/175071 https://dx.doi.org/10.3390/s19183975 |
| Access Level: | acceso abierto |
| Palabra clave: | Mobile communication systems 5G edge computing user plane function framework ILP optimization problem Comunicacions mòbils, Sistemes de Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
| Sumario: | Achieving less than 1 ms end-to-end communication latency, required for certain 5G services and use cases, is imposing severe technical challenges for the deployment of next-generation networks. To achieve such an ambitious goal, the service infrastructure and User Plane Function (UPF) placement at the network edge, is mandatory. However, this solution implies a substantial increase in deployment and operational costs. To cost-effectively solve this joint placement problem, this paper introduces a framework to jointly address the placement of edge nodes (ENs) and UPFs. Our framework proposal relies on Integer Linear Programming (ILP) and heuristic solutions. The main objective is to determine the ENs and UPFs’ optimal number and locations to minimize overall costs while satisfying the service requirements. To this aim, several parameters and factors are considered, such as capacity, latency, costs and site restrictions. The proposed solutions are evaluated based on different metrics and the obtained results showcase over 20% cost savings for the service infrastructure deployment. Moreover, the gap between the UPF placement heuristic and the optimal solution is equal to only one UPF in the worst cases, and a computation time reduction of over 35% is achieved in all the use cases studied. |
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