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Intelligent techniques are urged to achieve automatic allocation of thecomputing resource in Open Radio Access Network (O-RAN), to save computingresource, increase utilization rate of them and decrease the delay. However,the existing problem formulation to solve this resource allocation problem isunsuitable as it defines the capacity utility of resource in an inappropriateway and tends to cause much delay. Moreover, the existing problem has only beenattempted to be solved based on greedy search, which is not ideal as it couldget stuck into local optima. Considering those, a new formulation that betterdescribes the problem is proposed. In addition, as a well-known global searchmeta heuristic approach, an evolutionary algorithm (EA) is designed tailoredfor solving the new problem formulation, to find a resource allocation schemeto proactively and dynamically deploy the computing resource for processingupcoming traffic data. Experimental studies carried out on several real-worlddatasets and newly generated artificial datasets with more properties beyondthe real-world datasets have demonstrated the significant superiority over abaseline greedy algorithm under different parameter settings. Moreover,experimental studies are taken to compare the proposed EA and two variants, toindicate the impact of different algorithm choices.

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