We propose Auto-3P, an Autonomous module for Virtual Network Functions Performance Prediction and Placement at network cloud and edge facilities based on Machine Learning (ML). Auto-3P augments the autonomous placement capabilities of MANagement and Orchestration frameworks (MANOs) by considering both resource availability at hosting nodes and the implied impact of a VNF node placement decisions on the whole service level end-to-end performance. Unlike that, most existing placement methods take a rather myopic approach after manual rule-based decisions, and/or based exclusively on a host-centric view that focuses merely on node-local resource availability and network metrics. We evaluate and validate Auto-3P with real-field trials in the context of a well-defined Smart City Safety use case using a real end-to-end application over a real city-based testbed. We meticulously conduct repeated tests to assess (i) the accuracy of our adopted prediction models; and (ii) their placement performance against three other existing MANO approaches, namely, a “Traditional”, a “Latency-aware” and a “Random” one, as well as against a collection of well-known Time Series Forecasting (TSF) methods. Our results show that the accuracy of our ML models outperforms the one by TSF models, with the most prominent accuracy performances being exhibited by models such as K-Nearest Neighbors Regression (K-NNR), Decision Tree (DT), and Support Vector Regression (SVR). What is more, the resulted end-to-end service level performance of our approach outperforms “Traditional”, “Latency-aware”, and Random MANO placement. Last, Auto-3P achieves load balancing at selected VNF hosts without degrading end-to-end service level delay, and without a need for a (fixed) overload threshold check, unlike what is suggested by other works in the literature for coping with heavy system-wide load conditions.

我们建议自动3P，一个自动虚拟网络功能nomous模块P erformance P rediction和P lacement在基于机器学习（ML）网络云和边缘设备。通过考虑托管节点上的资源可用性以及VNF节点放置决策对整个服务级别的隐含影响，Auto-3P增强了管理和协调框架（MANO）的自主放置功能。端到端性能。与此不同的是，大多数现有的放置方法在基于手动规则的决策之后，和/或仅基于以主机为中心的视图（仅关注节点本地资源可用性和网络指标），采取了相当近视的方法。我们通过现场试验评估和验证Auto-3P在定义明确的智慧城市安全用例的背景下，使用基于真实城市的测试平台上的真实端到端应用程序。我们精心进行重复测试以评估（i）我们采用的预测模型的准确性；（ii）相对于其他三种现有MANO方法（即“传统”，“延迟感知”和“随机”）的放置性能，以及一组著名的时间序列预测（TSF）的放置性能方法。我们的结果表明，我们的ML模型的准确性优于TSF模型，其最显着的准确性表现在诸如K最近邻回归（K-NNR），决策树（DT）和支持向量回归之类的模型中（SVR）。而且，我们的方法所产生的端到端服务水平性能要优于“传统”，“等待时间感知”，和随机MANO放置。最后，Auto-3P在选定的VNF主机上实现负载平衡不会降低端到端的服务水平延迟，也不需要（固定的）过载阈值检查，这与文献中的其他工作所建议的那样，可以应对全系统的高负载条件。