Zenodo.org will be unavailable for 2 hours on September 29th from 06:00-08:00 UTC. See announcement.

Journal article Open Access

Big Data for 5G Intelligent Network Slicing Management

Chergui, Hatim; Verikoukis, Christos

Network slicing is a powerful tool to harness the full potential of 5G systems. It allows verticals to own and exploit independent logical networks on top of the same physical infrastructure. Motivated by the emergence of the big data paradigm, this article focuses on the enablers of big-databased intelligent network slicing. The article starts by revisiting the architecture of this technology that consists of data collection, storage, processing, and analytics before it highlights their relationship with network slicing concepts and the underlying trade-offs. It then proposes a complete framework for implementing big-data-driven dynamic slicing resource provisioning while respecting SLAs. This includes the development of low-complexity slices' traffic predictors, resource allocation models, and SLA enforcement via constrained deep learning. The article finally identifies the key challenges and open research directions in this emerging area.

Grant numbers : SPOT5G - Single Point of attachment communications heterogeneous mobile data networks (TEC2017-87456-P) and 5G-Solutions - 5G-Solutions (H2020-ICT-2018-3 // Grant agreement ID: 856691).© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Files (281.2 kB)
Name Size
Big Data for 5G Intelligent.pdf
281.2 kB Download
Views 38
Downloads 432
Data volume 121.5 MB
Unique views 38
Unique downloads 418


Cite as