Hyperbolic K-means for traffic-aware clustering in cloud and virtualized RANs - Cnam - Conservatoire national des arts et métiers Accéder directement au contenu
Article Dans Une Revue Computer Communications Année : 2021

Hyperbolic K-means for traffic-aware clustering in cloud and virtualized RANs

Résumé

As the internet and connected objects gain more and more in popularity, serving the ever-increasing data traffic becomes a challenge for the mobile operators. The traditional cellular radio access network (RAN), where each base station is co-located with its own processing unit and is responsible for a specific geographic area, has evolved first with the so-called Cloud RAN (C-RAN), and is currently undergoing further architectural evolution under the virtualized RAN (vRAN), Open RAN (O-RAN) and Software-Defined RAN (SD-RAN) architectures. In all these versions, the data processing units can be dynamically centralized into a pool and shared between several base stations, enlarging the geographical view for scheduling and resource allocation algorithms. For instance, resource utilisation is improved by avoiding resource idling during off-peak hours. C-RAN and vRAN gains depend strongly on the clustering scheme of radio units (RRHs and RUs). In this paper, we propose a novel radio clustering algorithm that takes into account both the traffic demand and the position of stations, by using the hyperbolic distance in 3dimensions. We introduce a modified K-means clustering algorithm, called Hyperbolic K-means, and show that this generates geographically compact RU clusters with traffic charge equally shared among them. Application of our algorithm on real-world mobile data traffic, collected from the cities of Nantes and Lille in France, shows an increase in resource utilisation by 25%, and a reduction in deployment cost by 15%, compared to the standard RAN. Furthermore, the performance of our Hyperbolic K-means algorithm is compared extensively against alternative C-RAN clustering proposals from the literature and is shown to outperform them, in resource utilisation as well as in cost reduction.
Fichier principal
Vignette du fichier
hyperboKv2.pdf (1.17 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03109662 , version 1 (13-01-2021)
hal-03109662 , version 2 (09-02-2021)
hal-03109662 , version 3 (13-07-2021)

Licence

Paternité - Pas d'utilisation commerciale - Partage selon les Conditions Initiales

Identifiants

Citer

Hanane Djeddal, Liticia Touzari, Anastasios Giovanidis, Chi-Dung Phung, Stefano Secci. Hyperbolic K-means for traffic-aware clustering in cloud and virtualized RANs. Computer Communications, 2021, 176, pp.258-271. ⟨10.1016/j.comcom.2021.06.021⟩. ⟨hal-03109662v2⟩
635 Consultations
321 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More