Dynamic Fast Convergence Improvement using Predictive Network Analysis

Abstract

In today's digital age, the smooth operation of organizations heavily relies on the proper functioning of the network infrastructure. Imagine a situation when a major change in the structure of a network causes the interruption of vital services. Consequently, the implementation of network convergence optimization is a vital consideration in practical situations. The aim of our study is to tackle existing issues by implementing a comprehensive approach that integrates predictive analysis. Implementing strategies for adaptive adjustment. Improving effectiveness using the Spanning Tree Protocol (STP). Our goal was to decrease the duration of convergence and improve the network's stability. The study will be undertaken by combining several machines learning techniques, including ARIMA, link prediction, and graph embedding. We performed real-time network monitoring. Utilizing predictive analysis to direct a process of adaptive convergence adjustments. The outcomes were positive, the upgraded STP solution considerably decreases convergence times. with 70% accuracy in forecasting low convergence times. 80% accuracy in forecasting high convergence times. Additionally, it delivers a large reduction in network disturbances. correctly anticipating low interruptions with 80% accuracy. high disruptions with 85% accuracy. Moreover, the approach maximizes resource use. successfully forecasting low usage with 75% accuracy and high utilization with 70% accuracy. Diagonal components suggest correct forecasts, whereas off-diagonal components suggest misclassifications. Overall, the matrix undervalues the solution's resilience. a tremendous positive influence on network stability and efficiency.