Traffic flow prediction in cloud computing

Abstract

Cloud computing provides improved and simplified IT management and maintenance capabilities through central administration of resources, companies of all shapes and sizes are adapting to this new technology. In the absence of an effective prediction tools of cloud computing traffic then allocation of resources to clients will be ineffective thus driving away cloud computing users. In this paper we propose auto-regressive integrated moving average (ARIMA) and artificial neural networks (ANN) as prediction tools for cloud computing traffic. The results show that ARIMA performs better than ANN in predicting cloud computing traffic. For future work we propose to investigate the use of a hybrid of the two in predicting cloud computing traffic.