Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (12): 125-135.doi: 10.11896/jsjkx.220200106

• Computer Software • Previous Articles     Next Articles

Study on Anomaly Detection and Real-time Reliability Evaluation of Complex Component System Based on Log of Cloud Platform

WANG Bo1,2,3, HUA Qing-yi1, SHU Xin-feng2   

  1. 1 School of Information Science and Technology,Northwestern University,Xi’an 710119,China
    2 School of Computer Science,Xi’an University of Posts and Telecommunications,Xi’an 710121,China
    3 Shaanxi Key Laboratory of Network Data Intelligent Processing,Xi’an University of Posts and Telecommunications,Xi’an 710121,China
  • Received:2022-02-17 Revised:2022-06-05 Published:2022-12-14
  • About author:WANG Bo,born in 1976,Ph.D,lectu-rer,is a member of China Computer Fe-deration.His main research interests include system reliability,software engineering and human-computer interaction.HUA Qing-yi,born in 1956,Ph.D,professor.His main research interests include human-computer interaction,re-commender systems,and user interface engineering.
  • Supported by:
    Key Science and Technology Program of Shaanxi Province,China(2016GY-123),Key Research and Development Projects of Shaanxi Province(2020GY-210),Industrial Science and Technology Research Project of Henan Province(212102210418) and National Natural Science Foundation of China(61272286).

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Abstract: Reliability,usability and security are three important indicators of software quality measurement,and software reliability is the most important indicator.Software system is regarded as a whole or viewed invocation structure of software as static structure in traditional software reliability evaluation and prediction.Today’s software architecture has changed significantly.Typical features such as autonomy,coordination,evolution,dynamic and adaptive have been infiltrated into the current complex network software system.Traditional reliability evaluation and prediction methods cannot adapt to such software architecture or environment.Currently,in the society of high-speed information,“software defines everything”.Massive information systems ge-nerate large-scale data resources.The diversity and complexity of log resources are the results of heterogeneity,parallelism,complexity and huge scale of modern information systems.Accurate analysis and anomaly prediction based on logs are particularly important for building safe and reliable systems.There are a lot of research on anomaly prediction and software reliability in the existing literatures,but there is little about real-time software reliability measurement for massive logs and complex network component systems.Accordingly,based on the complete procedures of log processing,from its analysis,feature extraction,anomaly detection and prediction evaluation to real-time reliability evaluation,this paper uses ensemble learning model to analyze and predict anomaly of the massive system logs.Comparisons with the traditional machine learning methods are made to improve the accuracy,recall rate and F1 value of anomaly prediction.The evaluation result is used to correct the real-time reliability in view of the low predicted recall rate,which greatly improves the accuracy of real-time reliability.According to the individual reliability,the system reliability based on Markov theory is used to measure the reliability of microservice composite components,so as to provide accurate data basis and anomaly location basis for intelligent operation and maintenance.

Key words: Log parsing, Anomaly detection, Reliability evaluation, Root cause analysis, Ensemble learning, Complex components

CLC Number: 

  • TP311.5
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