Evolutionary Optimization for Proactive and Dynamic Computing Resource Allocation in Open Radio Access Network

In Open Radio Access Network (O-RAN), intelligent techniques are urged to achieve the automation of the computing resource allocation, so as to save computing resources and increase their utilization rate, as well as decrease the network delay. However, the existing formulation of this problem as an optimization problem defines the capacity utility of resource in an inappropriate way and it tends to cause much delay. Moreover, the only algorithm proposed to solve this problem is a greedy search algorithm, which is not ideal as it could get stuck into local optima. To overcome these issues, a new formulation that better describes the problem is proposed. In addition, an evolutionary algorithm (EA) is designed to find a resource allocation scheme to proactively and dynamically deploy the computing resource for processing upcoming traffic data. A multivariate long shortterm memory model is used in the proposed EA to predict future traffic data for the production of deployment scheme. As a global search approach, the EA is less likely to get stuck in local optima than greed search, leading to better solutions. Experimental studies carried out on real-world datasets and artificially generated datasets with different scenarios and properties have demonstrated the significant superiority of our proposed EA over a baseline greedy algorithm under all parameter settings. Moreover, experimental studies with all afore-mentioned datasets are performed to compare the proposed EA and two variants under different parameter settings, to demonstrate the impact of different algorithm choices.