An Efficient and Balanced BBU Computing Resource Allocation Algorithm for Cloud Radio Access Networks

This paper considers the BBU computing resource allocation problem in a C-RAN system. In particular, we consider the additional computing resource consumption introduced for coordination between different BBUs when the tasks of a cell is processed by different BBUs and consider load balancing among multiple BBUs in order to reduce the effect of a breakdown BBU on the system. The problem is formulated as an optimization problem with the objective to minimize the total amount of computing resources used for processing the tasks of all cells and meanwhile balance the allocation of the computing resources in all used BBUs. The formulated problem is equivalent to the classical bin-packing problem, which has proved to be NP-hard. To solve the problem, a heuristic genetic algorithm (HeuGA) is proposed to find an approximate solution to the problem. HeuGA combines a genetic algorithm (GA) with a first-fit (FF) algorithm, and consists of four steps: population initialization, fitness function construction, individual selection, and cross and mutation. Simulation results show that HeuGA outperforms the FF algorithm and the GA algorithm in terms of the number of BBUs used in the system and BBU resource allocation balancing without affecting the efficiency of the resource allocation.