A Novel Hybridization of Artificial Neural Networks and ARIMA Models for Forecasting Resource Consumption in an IIS Web Server
Yongquan Yan, Ping Guo, Lifeng Liu · 2014
Software aging has been observed in a long running software application. A technique named rejuvenation is proposed to counteract this problem. The key to the aging and rejuvenation problem is how to analyze/forecast the resource consumption of software system. In this paper, we propose a methodology of hybrid ARIMA and artificial neural networks to forecast resource consumption in an IIS web server which is a running commercial server and subjected to software aging. The proposed hybrid method consists of two steps. In the first step, an ARIMA model is used to analyze the linear component of the data. In the second step, an artificial neural network model is developed to model the residuals from ARIMA model. The results show that the proposed hybrid model can be a good trade-off to forecast resource consumption.