End-to-end intent-based networking
Velasco, Luis; Signorelli, Marco; González De Dios, Oscar; Papagianni, Chrysa; Bifulco, Roberto; Olmos, Juan Jose Vegas; Pryor, Simon; Carrozzo, Gino; Schulz-Zander, Julius; Bennis, Mehdi; Martinez, Ricardo; Cugini, Filippo; Salvadori, Claudio; Lefebvre, Vincent; Valcarenghi, Luca; Ruiz, Marc (2021-10-01)
L. Velasco et al., "End-to-End Intent-Based Networking," in IEEE Communications Magazine, vol. 59, no. 10, pp. 106-112, October 2021, doi: 10.1109/MCOM.101.2100141.
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https://urn.fi/URN:NBN:fi-fe2023030730248
Tiivistelmä
Abstract
To reap its full benefits, 5G must evolve into a scalable decentralized architecture by exploiting intelligence ubiquitously and securely across different technologies, network layers, and segments. In this article, we propose end-to-end and ubiquitous secure machine learning (ML)-powered intent-based networking (IBN). The IBN framework is aware of its state and context to autonomously take proactive actions for service assurance. It is integrated in a zero-touch control and orchestration framework featuring an ML function orchestrator to manage ML pipelines. The objective is to create an elastic and dynamic infrastructure supporting per-domain and end-to-end network and services operation. The solution is supported by a radio access network and forwarding plane, and a cloud/edge virtualization infrastructure with ML acceleration. The resulting framework supports application-level resilience and intelligence through replication and elasticity. An illustrative intelligent application use case is presented.
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