论文标题
虚拟网络功能实例的基于机器学习的迁移策略
A Machine Learning-Based Migration Strategy for Virtual Network Function Instances
论文作者
论文摘要
随着对数据连接的需求不断增长，网络服务提供商面临减少其资本和运营费用的任务，同时提高网络绩效并满足需求的增加。尽管网络功能虚拟化（NFV）已被确定为有前途的解决方案，但必须解决一些挑战以确保其可行性。在本文中，我们通过开发VNF神经网络（例如迁移（VNNIM））来解决虚拟网络功能（VNF）迁移问题，这是VNF实例的迁移策略。通过优化粒子群的优化，通过优化学习率超计的优化，VNNIM的性能得到进一步提高。结果表明，VNNIM非常有效地预测二进制精度为99.07％的二进制精度和与优化模型相比，二进制精度为99.07％的二进制精度和延迟差分布。但是，VNNIM的最大优势是通过运行时分析突出显示其运行时效率。
With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased demand. Although Network Function Virtualization (NFV) has been identified as a promising solution, several challenges must be addressed to ensure its feasibility. In this paper, we address the Virtual Network Function (VNF) migration problem by developing the VNF Neural Network for Instance Migration (VNNIM), a migration strategy for VNF instances. The performance of VNNIM is further improved through the optimization of the learning rate hyperparameter through particle swarm optimization. Results show that the VNNIM is very effective in predicting the post-migration server exhibiting a binary accuracy of 99.07% and a delay difference distribution that is centered around a mean of zero when compared to the optimization model. The greatest advantage of VNNIM, however, is its run-time efficiency highlighted through a run-time analysis.
