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Intelligent Automated NFV Deployment with Optimized VNF Placement

Authors:

Dr.Granty Regina Elwin, Dr.Latha Maheshwari T, Kavya Kumar, Harsha M, Ameen Sheriff

Abstract:

This project focuses on developing an intelligent and automated system for Network Function Virtualization (NFV) deployment with optimized Virtual Network Function (VNF) placement. The system leverages machine learning techniques to continuously monitor traffic patterns and detect overloaded or underutilized nodes within the network. Upon identifying congestion or node failure, the model dynamically adjusts the network topology by deploying new nodes and rerouting traffic to ensure optimal load balancing and efficient resource utilization. For instance, when a node becomes overloaded, additional nodes are introduced, and routing paths are intelligently modified to alleviate network bottlenecks. The system also incorporates a user-friendly control panel, providing real-time visibility into network metrics, traffic loads, and routing strategies, while offering manual control capabilities. This solution aims to enhance network stability, minimize latency, and improve overall service delivery by automating NFV deployment and VNF placement processes.

Keywords: NFV deployment, VNF placement, machine learning, traffic monitoring, load balancing, node failure detection, dynamic node deployment, traffic rerouting, network optimization, control panel, real-time monitoring, network stability, resource utilization, service delivery, automated system.