Predicting resource consumption in a web server using ARIMA model  

作　　者：闫永权 郭平 

机构地区：[1]School of Computer Science and Technology,Beijing Institute of Technology

出　　处：《Journal of Beijing Institute of Technology》2014年第4期502-510,共9页北京理工大学学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(60911130513,60805004)

摘　　要：Software aging is a phenomenon observed in a software application executing continuous- ly for a long period of time, where the state of software degrades and leads to performance degrada- tion, hang/crash failures or both. A technique named rejuvenation was proposed to counteract this problem. Rejuvenation in period is not a good idea, because the speed of software aging is not constant, but variable. The key to find an optimal timing to resist aging problem is how to analyze/fore- cast the resource consumption of aging system. An ARIMA model is applied to forecast resource con- sumption due to software aging in a running web server. First, order and parameters of ARIMA model need to be identified. Second, it needs to be checked whether the model satisfies stationarity and reversibility. Finally, ARIMA model is used to predict resource consumption. The experiment results indicate that ARIMA model can do better than ANN model and SVM model in the forecasts of available memory and heap memory.Software aging is a phenomenon observed in a software application executing continuous- ly for a long period of time, where the state of software degrades and leads to performance degrada- tion, hang/crash failures or both. A technique named rejuvenation was proposed to counteract this problem. Rejuvenation in period is not a good idea, because the speed of software aging is not constant, but variable. The key to find an optimal timing to resist aging problem is how to analyze/fore- cast the resource consumption of aging system. An ARIMA model is applied to forecast resource con- sumption due to software aging in a running web server. First, order and parameters of ARIMA model need to be identified. Second, it needs to be checked whether the model satisfies stationarity and reversibility. Finally, ARIMA model is used to predict resource consumption. The experiment results indicate that ARIMA model can do better than ANN model and SVM model in the forecasts of available memory and heap memory.

关 键 词：software aging software rejuvenation ARIMA web server 

分 类 号：TP393.09[自动化与计算机技术—计算机应用技术]