论文标题
深度优化的延迟感知树（DO-DAT）用于虚拟网络功能放置
Depth-Optimized Delay-Aware Tree (DO-DAT) for Virtual Network Function Placement
论文作者
论文摘要
随着对数据连接的需求不断增加，网络服务提供商面临着减少其资本和运营费用的任务，同时确保不断改进网络性能。尽管网络功能虚拟化（NFV）已被确定为解决方案，但必须解决一些挑战以确保其可行性。在本文中，我们为虚拟网络功能（VNF）放置问题提供了基于机器学习的解决方案。本文提出了深度优化的延迟感知树（DO-DAT）模型，该模型通过使用粒子群优化技术来优化决策树超参数。使用进化的数据包核心（EPC）作为用例，我们评估模型的性能，并将其与先前提出的模型和启发式放置策略进行比较。
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.
