Real Traffic-Aware Scheduling of Computing Resources in Cloud-RAN

Hatem Khedher (1) , Sahar Hoteit (1) , Patrick Brown (2) , Véronique Vèque (1) , Ruby Krishnaswamy (3) , William Diego (4) , Makhlouf Hadji (5)

1 L2S - Laboratoire des signaux et systèmes
2 Orange Labs [Lannion]
3 Orange Labs [Issy les Moulineaux]
4 Orange Labs [Chatillon]
5 IRT SystemX

Cloud-Radio Access Network (C-RAN) is a promising mobile network architecture that is becoming the foundation of 5G wireless network. It permits to centralize the computing resources in the Baseband Unit (BBU) pool which adds more flexibility and increases network performance. However, as computing resources are now shared among the Radio Remote Heads (RRHs) connected to the BBU pool, efficient scheduling algorithms should be explored in order to meet the deadlines requirements of RRHs' subframes and to increase network throughput. In this paper, we propose optimal scheduling algorithms for computing resources along with three heuristics. We test the different algorithms as a function of three performance metrics such as the offered throughput, the computing resources occupancy and the number of non-decoded subframes. The evaluation is performed under a real traffic model for the incoming subframes. The obtained results highlight the importance of choosing the appropriate scheduling algorithm and bring recommendations to mobile network operators on the best scheduling algorithm that should be adopted to increase network performance.

Domaines: Réseaux et télécommunications [cs.NI]

Hatem Khedher, Sahar Hoteit, Patrick Brown, Véronique Vèque, Ruby Krishnaswamy, et al.. Real Traffic-Aware Scheduling of Computing Resources in Cloud-RAN. 2020 International Conference on Computing, Networking and Communications (ICNC), Feb 2020, Big Island, United States. pp.422-427, ⟨10.1109/ICNC47757.2020.9049679⟩. ⟨hal-02575252⟩