Application Performance Anomaly Detection Based on LSTM Prediction Model
At present,the scale and complexity of high-performance computing systems are constantly increasing,and the reasons for abnormal job performance of application software have become more complex and diverse.Tradi-tional methods for manual analysis based on monitoring data have problems of low efficiency and excessive reliance on the experience of analysts.This Propose a performance anomaly detection method based on Long Short Term Memory Network(LSTM).Taking the weather forecast model WRF as the research object,we first learn the changes in normal performance data from historical homework data,and then introduce the boxplot method to statistically analyze the re-sidual between the predicted values of the LSTM model and the actual observed values.Data larger than the lower quartile is judged as abnormal,thus achieving the detection of abnormal performance in application software homework.The experimental results show that this method can not only effectively alleviate the shortage of manual method,but also can be applied to various types of data sets.
Application performance anomaly detectionLong short-term memoryAuto regressive moving average modelWeather forecast model
NETL
NSTL
万方数据
