Currently, telecommunication research communities are striving towards the adoption of Zero-touch network and Service Management (ZSM) in Network Function Virtualisation (NFV) orchestration. Contemporary efforts on adopting Machine Learning (ML) and Artificial Intelligence (AI) have caused an upsurge of ZSM application in the VNF space. While ML and AI complement the ZSM goals for building the intelligent NFV orchestration, a deep knowledge about the resource consumption by Network Services (NSs) and its constituent Virtual Network Functions (VNFs) is required, which would enable AI and ML models to manage the available resources better and enhance user experience. In this article, we propose a Novel Autonomous Profiling ( NAP ) method that not only predicts the optimum network load a VNF can support but also estimates the required resources in terms of CPU, Memory, and Network, to meet the performance targets and workload by utilising ML techniques. Our performance evaluation results on real datasets show that the output of NAP can be used in the next generation of NFV orchestration.

中文翻译：

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下一代NFV编排器的一种新颖的自动剖析方法

当前，电信研究社区正在努力在网络功能虚拟化（NFV）编排中采用零接触网络和服务管理（ZSM）。在采用机器学习（ML）和人工智能（AI）方面的当代努力引起了ZSM在VNF空间中的应用热潮。虽然ML和AI补充了构建智能NFV编排的ZSM目标，但需要对网络服务（NS）及其组成的虚拟网络功能（VNF）的资源消耗有深入的了解，这将使AI和ML模型能够管理更好地利用可用资源并增强用户体验。在本文中，我们提出了一种新颖的自治配置文件（ 小憩 ）方法，不仅可以预测VNF可以支持的最佳网络负载，还可以根据CPU，内存和网络来估计所需的资源，以通过使用ML技术来满足性能目标和工作负载。我们在真实数据集上的性能评估结果表明，NAP的输出可用于下一代NFV编排。