Volume 18, Number 1

An Efficient Federated Forcart Algorithm for Predicting and Allocating Resources in Network Function Virtualization

  Authors

Kavitha.A and Dr.Gobi.M , Chikkanna Government Arts College, India

  Abstract

Network Function Virtualization (NFV) is a traditional network method, it’s replacing rigid, dedicated hardware devices with flexible, software-based virtual network functions that can be dynamically assigned and scaled across any networking infrastructure. Even though these findings give promise, still in the early stages of creating network management systems that are truly dependable, can handle massive scale, and keep sensitive information protected across different networking boundaries. While this sounds great in theory, there is a problem with how we currently use artificial intelligence to manage these systems. Traditional AI approaches, specifically reinforcement learning models, hit several problems. However, these methods often provide high computational overhead, slow convergence, and suffer from limited interpretability. In this paper, we propose a novel framework that replaces DRL with lightweight and interpretable Machine Learning (ML) algorithms. This article suggests a Federated Forcart-based NFV Resource Allocation Framework that combines a hybrid Random Forest–Cart prediction model with federated learning to address these issues. The framework supports balanced multi-metric resource optimization, precise workload prediction, and distributed knowledge exchange while maintaining local data secrecy. It maintains the parallelization strategy to minimize end-to-end latency, while significantly reducing training complexity and communication overhead. Improved prediction accuracy, decreased latency, and increased CPU and energy efficiency are demonstrated by comparison with state-of-the-art methods. For next-generation NFV orchestration, the suggested method offers a scalable and private solution.

  Keywords

NFV, Prediction, Federated Learning, forcart, Resource allocation.