Abstract
In cloud radio access networks (C-RAN), more accurate prediction of the number of virtual machines (VMs) one server can support would improve network capacity and energy efficiency (EE). In this paper, the problem of allocating an optimal number of VMs to the cloud server is introduced. Monte Carlo-based evolutionary algorithm [particle swarm optimization (PSO), quantum PSO, or genetic algorithm] are used to find the suboptimal number of VMs that optimizes the EE of C-RAN. To enable such evaluation, a power model is proposed to evaluate the power consumption of each unit within a virtualized server. This evaluation occurs under the circumstances of increased number of hosted VMs, and processed resource blocks (RBs) at each VM. Moreover, power allocation methods are proposed to transmit the power from base band unit pool to the remote radio heads (RRHs), and from RRHs to the users (UEs). This allocation is based on the combination of one or more of RRH distance, RRH channel gain, UE distance, UE channel gain, and UE path loss. The EE problem was constrained to crucial quality of service indicators, including minimum UE data rate, number of allocated RBs, and latency imposed due to virtualization.
Highlights
D RIVEN by the need to provide at least 10 times higher spectral and energy efficiency (EE) in 5G networks, mobile operators deployed a large number of small cells in heterogeneous networks
In cloud-radio access network (C-RAN), the base band units (BBUs) servers are responsible for processing the upper layers and most of the physical layer functions, including radio frequency (RF), base band digital signal processing, Manuscript received February 5, 2018; revised May 18, 2018; accepted July 21, 2018
The proposed model is not constrained to only yielding this amount of percentage, but rather, is valid for any type of server through adjusting the model parameters
Summary
D RIVEN by the need to provide at least 10 times higher spectral and energy efficiency (EE) in 5G networks, mobile operators deployed a large number of small cells in heterogeneous networks Whilst this has increased network capacity, it has led to the consumption of more power. Each VM can utilise the server’s random access memory (RAM), central processing unit (CPU), network interface cards (NICs) and hard drive (HDD) by itself without obstructing other VMs. First, the HV collects the information of each VM regarding the number of UEs and their QoS indicators, subsequently scheduling the available resource blocks (RBs) amongst these VMs. Afterwards, each VM can schedule its share of RBs amongst its UEs according to different QoS factors such as, minimum data rate, received power, interference, etc. A detailed comparison of the virtualised and bare servers is required to identify the advantages and disadvantages of using NFV in C-RAN
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