Elastic Resource Provisioning for Increased Energy Efficiency and Resource Utilization in Cloud-RANs

Abstract

Distributed Radio Access Networks (D-RANs), characterized by static deployment of Base Stations (BSs), are facing critical difficulties in handling the temporal and geographical fluctuations of capacity demands. Recently, Cloud RAN (C-RAN) has been introduced as a centralized cloud computing based paradigm for wireless cellular networks in which the BSs are physically decoupled into Virtual Base Stations (VBSs) and Remote Radio Heads (RRHs). In this article, a novel framework is proposed for C-RAN to adapt to the fluctuation in capacity demand while at the same time maximizing the energy efficiency and resource utilization. The RRHs and their corresponding VBSs are divided into clusters within which the active RRH density, transmission power, and size of Virtual Machines (VMs) are adjusted dynamically. To characterize the computational requirements of a VBS, a programmable C-RAN testbed is implemented using the OpenAirInterface (OAI) software platform and USRP boards. Testbed experiment results are presented to describe the consumption and utilization of computing resources in the cloud; simulation results are also presented to illustrate the performance gains of this elastic solution against the current static deployment.