various techniques have been proposed to reduce network traffic and improve the quality of service for mobile cloud computing. These techniques include prefetching, data compression, offloading, intelligent access schemes, and machine learning-based request classification. Prefetching involves retrieving data from cloud servers in advance based on previous user log data, while data compression aims to reduce the size of data transmitted across the network. Offloading involves offloading computation tasks from mobile devices to more powerful cloud servers to reduce the workload on the device and improve its performance. In this paper, we have applied machine learning techniques on the pre-processed 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. The most effective algorithm for classifying client requests and generating rules to minimize network traffic. It is observed that Random Forest has highest accuracy rate of 77.21% compared with other algorithms.

Random Forest, data compression, reprocessed data