In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning ha...In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning has been shown as a very promising technique in application to forecast software state: normal or aging. In this paper, we proposed a method which can give practice guide to forecast software aging using machine learning algorithm. Firstly, we collected data from a running commercial web server and preprocessed these data. Secondly, feature selection algorithm was applied to find a subset of model parameters set. Thirdly, time series model was used to predict values of selected parameters in advance. Fourthly, some machine learning algorithms were used to model software aging process and to predict software aging. Fifthly, we used sensitivity analysis to analyze how heavily outcomes changed following input variables change. In the last, we applied our method to an IIS web server. Through analysis of the experiment results, we find that our proposed method can predict software aging in the early stage of system development life cycle.展开更多
The authors studied the potential field boundary identification of the new technology in order to find out the possible fractures or contact zones using the following methods such as tilt derivative,horizontal derivat...The authors studied the potential field boundary identification of the new technology in order to find out the possible fractures or contact zones using the following methods such as tilt derivative,horizontal derivative of tilt derivative,normalized standard deviation and normalized differential method. Combined with Euler deconvolution and small subdomain filtering,the actual data processing results show that these methods are all able to identify wider range extending fractures and obtain abundant geological information. The horizontal derivative of tilt derivative and normalized differential method have a better resolution for the small cutting fractures and lacunae in the studied area. They provide a reliable basis for study of the cutting relationship between fractures.展开更多
基金supported by the grants from Natural Science Foundation of China(Project No.61375045)the joint astronomic fund of the national natural science foundation of China and Chinese Academic Sinica(Project No.U1531242)Beijing Natural Science Foundation(4142030)
文摘In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning has been shown as a very promising technique in application to forecast software state: normal or aging. In this paper, we proposed a method which can give practice guide to forecast software aging using machine learning algorithm. Firstly, we collected data from a running commercial web server and preprocessed these data. Secondly, feature selection algorithm was applied to find a subset of model parameters set. Thirdly, time series model was used to predict values of selected parameters in advance. Fourthly, some machine learning algorithms were used to model software aging process and to predict software aging. Fifthly, we used sensitivity analysis to analyze how heavily outcomes changed following input variables change. In the last, we applied our method to an IIS web server. Through analysis of the experiment results, we find that our proposed method can predict software aging in the early stage of system development life cycle.
文摘The authors studied the potential field boundary identification of the new technology in order to find out the possible fractures or contact zones using the following methods such as tilt derivative,horizontal derivative of tilt derivative,normalized standard deviation and normalized differential method. Combined with Euler deconvolution and small subdomain filtering,the actual data processing results show that these methods are all able to identify wider range extending fractures and obtain abundant geological information. The horizontal derivative of tilt derivative and normalized differential method have a better resolution for the small cutting fractures and lacunae in the studied area. They provide a reliable basis for study of the cutting relationship between fractures.
