Enabling traffic forecasting with cloud-native SDN controller in transport networks


Por: Adanza D., Gifre L., Alemany P., Fernández-Palacios J.-P., González-de-Dios O., Muñoz R., Vilalta R.

Publicada: 1 jun 2024 Ahead of Print: 1 jun 2024
Resumen:
Network bandwidth is a scarce resource that network operators monitor to cope with future traffic demands and plan more transceiver and fibre deployments. The inclusion of Machine Learning permits the usage of traffic forecasting methods to predict future link usage. Typically, traffic analysis is performed offline due to the high computational load and difficulty of obtaining real-time data directly from the underlying network devices. To overcome these limitations, this paper presents and evaluates an architecture for SDN-controlled packetoptical transport networks to allow real-time traffic monitoring in the transport SDN controller. The presented SDN controller is based on a micro-service-based architecture, which facilitates the ease of deployment of the proposed solution. Four forecasting methods are proposed and evaluated against two topologies to select the most precise and the fastest among them.The algorithm random forest seems to be the most accurate forecasting future link usage with 79.98 % and 95.88 % accuracy and a reasonable fast speed when implemented it into two different topologies © 2024 Elsevier B.V.

Filiaciones:
Adanza D.:
 Centre Tecnològic de Telecomunicacions de Catalunya - CERCA (CTTC-CERCA), Casteldefells, Spain

Gifre L.:
 Centre Tecnològic de Telecomunicacions de Catalunya - CERCA (CTTC-CERCA), Casteldefells, Spain

Alemany P.:
 Centre Tecnològic de Telecomunicacions de Catalunya - CERCA (CTTC-CERCA), Casteldefells, Spain

Fernández-Palacios J.-P.:
 Telefónica Innovación Digital (TID), Madrid, Spain

González-de-Dios O.:
 Telefónica Innovación Digital (TID), Madrid, Spain

Muñoz R.:
 Centre Tecnològic de Telecomunicacions de Catalunya - CERCA (CTTC-CERCA), Casteldefells, Spain

Vilalta R.:
 Centre Tecnològic de Telecomunicacions de Catalunya - CERCA (CTTC-CERCA), Casteldefells, Spain
ISSN: 13891286
Editorial
Elsevier, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, Países Bajos
Tipo de documento: Article
Volumen: 250 Número:
Páginas:
WOS Id: 001333615300001
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