Combined Linear Multi-Model for Reliable Route Recommender in Next Generation Network

Network analysis is a promising field in the area of network applications as different types traffic grow enormously and exponentially. Reliable route prediction challenging task Large Scale Networks (LSN). Various non-self-learning self-learning approaches have been adopted to predict reliable routing. Routing protocols decide how send all packets from source destination addresses across through their IP. In current era, dynamic are preferred they internally using an algorithm may not entail being updated physically more than static protocols. A novel method named Route Prediction Model (RRPM) proposed find best routes given hefty gage balance load entire advance recital. The carried out two phases. first phase, Embedding (NE) based node classification out. second phase involves LSN. experiment for average data transmission rerouting time measured between RRPM Information Protocol (RIP) protocol models with before after failure links. It was observed that RIP has 18.5 ms 18.2 ms. Hence model outperforms well traditional finding such Open Shortest Path First (OSPF).