  Intelligent techniques are urged to achieve automatic allocation of the
computing resource in Open Radio Access Network (O-RAN), to save computing
resource, increase utilization rate of them and decrease the delay. However,
the existing problem formulation to solve this resource allocation problem is
unsuitable as it defines the capacity utility of resource in an inappropriate
way and tends to cause much delay. Moreover, the existing problem has only been
attempted to be solved based on greedy search, which is not ideal as it could
get stuck into local optima. Considering those, a new formulation that better
describes the problem is proposed. In addition, as a well-known global search
meta heuristic approach, an evolutionary algorithm (EA) is designed tailored
for solving the new problem formulation, to find a resource allocation scheme
to proactively and dynamically deploy the computing resource for processing
upcoming traffic data. Experimental studies carried out on several real-world
datasets and newly generated artificial datasets with more properties beyond
the real-world datasets have demonstrated the significant superiority over a
baseline greedy algorithm under different parameter settings. Moreover,
experimental studies are taken to compare the proposed EA and two variants, to
indicate the impact of different algorithm choices.
