Machine learning algorithm-based minimisation of network traffic in mobile cloud computing

Abstract

Mobile cloud computing is an emerging technology where mobile device is integrated with cloud computing. It has many applications such as social network, online shopping, Flickr, Picasa. Besides these applications, it suffers from network traffic issue. The demand of the users has been increasing day by day but due to the limited density on base stations, it has become an overhead to the network service providers to provide the service. In this paper, we have applied machine learning techniques on the preprocessed data to classify client requests and generated rules to accept or to discard a client request. We aimed to minimize network traffic. We have applied J48, Naïve Bayes, Multi-Boosting AB, Simple Logistic Regression, Random Forest. It is observed that Random Forest has highest accuracy rate of 86.36% compared with other algorithms.