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With the constant increase in demand for data connectivity, network serviceproviders are faced with the task of reducing their capital and operationalexpenses while ensuring continual improvements to network performance. AlthoughNetwork Function Virtualization (NFV) has been identified as a solution,several challenges must be addressed to ensure its feasibility. In this paper,we present a machine learning-based solution to the Virtual Network Function(VNF) placement problem. This paper proposes the Depth-Optimized Delay-AwareTree (DO-DAT) model by using the particle swarm optimization technique tooptimize decision tree hyper-parameters. Using the Evolved Packet Core (EPC) asa use case, we evaluate the performance of the model and compare it to apreviously proposed model and a heuristic placement strategy.

Depth-Optimized Delay-Aware Tree (DO-DAT) for Virtual Network Function Placement