An Effective Methodology for Cloud-Based Priority Generation System using an ANFIS-CNN Classification Architecture

Abstract

The cloud architecture improves the memory capacity and optimizes bandwidth across the entire network architecture, facilitating the transmission and the reception of large amounts of multimedia data. The cloud system requires workflow computation and a scheduling algorithm to enhance the network performance. Therefore, this algorithm generates the priority of each multimedia data stream for effective transmission over the wireless network. In this research, a priority (PR) generation system is proposed for the efficient transmission of large multimedia videos over wireless networks through cloud architecture. This proposed system involves preprocessing video frames, computing features, and classifying them using the ANFIS-CNN classifier. This classifier is the integration of an adaptive neuro-fuzzy inference system (ANFIS) and the Convolutional Neural Network (CNN) classifier. The ANFIS-CNN based priority generation algorithm has been tested on two independent cloud platforms: Microsoft Azure and Amazon EC2. Experiment results from these cloud platforms have been compared with other existing algorithms for the priority generation process.