Depth-Optimized Delay-Aware Tree (DO-DAT) for Virtual Network Function Placement
This addresses the challenge of reducing costs and improving performance for network service providers through NFV, but appears incremental as it builds on prior models with a hybrid optimization approach.
The paper tackles the Virtual Network Function (VNF) placement problem in network service providers by proposing the Depth-Optimized Delay-Aware Tree (DO-DAT) model, which uses particle swarm optimization to optimize decision tree hyper-parameters, and evaluates it against existing models and heuristics in an Evolved Packet Core use case.
With the constant increase in demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while ensuring continual improvements to network performance. Although Network 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-Aware Tree (DO-DAT) model by using the particle swarm optimization technique to optimize decision tree hyper-parameters. Using the Evolved Packet Core (EPC) as a use case, we evaluate the performance of the model and compare it to a previously proposed model and a heuristic placement strategy.