An Extensive Analysis on Predictive Modeling for Network Traffic using Deep Learning Algorithms

In the era of proliferating AI applications and interconnected devices, traditional bandwidth forecasting methods fall short. This research explores the fusion of AI, particularly machine learning and deep learning algorithms, to redefine predictive modeling for network traffic. The goal is to develop an advanced forecasting model that flexibly adjusts to evolving network dynamics, accommodating diverse AI applications and user behaviors. The envisioned model aims to equip network administrators with AI-driven insights for accurate resource allocation, enhancing efficiency and responsiveness amid the escalating demands of AI-driven technologies. This study contributes to bridging the gap between burgeoning network requirements and existing forecasting methodologies, paving the way for more resilient and adaptive network infrastructures in the age of AI proliferation