Enhancing Virtualized Network Functions through Advanced Machine Learning for Efficient Resource Allocation and Dynamic Placement

This paper addresses the challenges of Network Functions Virtualization (NFV) with a specific focus on the optimization of Virtualized Network Functions (VNFs) through advanced machine learning techniques. As NFV evolves to support modern network demands, including the upcoming transition from 5G to 6G technologies, efficient resource allocation and dynamic VNF placement become crucial. Traditional methods often fail to meet these requirements due to their static nature and inability to adapt to changing network conditions dynamically. we introduce a comprehensive framework including the integration of multiple machine learning models (i.e., Random Forest, Decision Trees, SVR) combined into ensemble approach such as Linear regression to predict and optimize VNF performance dynamically. This scheme not only improves the reliability of resource placement, but also is conducive to realizing the automatic scalable VNF deployment for ZSM: Zero-touch network and Service Management. Here, all of these highlight that the ensemble model is better than the individual predictive machine models in predicting/amodeling network conditions as well as adjusting resources efficiently. The proposed model achieves very good results, improving in all the relevant key performance metrics RMSE, R2 and Explained Variance), and thus demonstrating that machine learning can bring deep changes to NFV environments. Though the study demonstrates the potential amplitude of these models, it also reveals significant challenges in bringing them to deployment levels, in specific extreme MAPE (Mean Absolute Percentage Error) cases. As such it opens doors for more research and development into this area too This work aims to expand the horizon of intelligent network management through actionable knowledge and guides from machine learning on improving the effectiveness and efficiency of network services supporting further adaptive, user-oriented networking schemes.