LAUSR

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.