Research And Implementation Of Monitoring System Based On Time Series Anomaly Detection

As more and more businesses and organizations expand their operations to a global scale,the management of their IT infrastructure and services becomes increasingly complex,making the development of efficient monitoring systems a hot topic.Monitoring systems can collect,analyze,and process data in real-time or at regular intervals for specific objects,systems,or services.They can notify relevant personnel promptly when abnormal conditions are detected,minimizing the impact of service interruptions caused by such conditions on enterprises and users.The accuracy of anomaly detection in monitoring systems,the efficiency of alarm generation for monitoring a large number of monitoring items,and the automation of software deployment and upgrades have all become urgent problems that need to be addressed in efficient monitoring systems.To address these challenges,this paper proposes a mechanism based on time series anomaly detection and designs and implements a monitoring system based on this mechanism.The main contributions of this work are as follows:(1)Algorithms and decision-making mechanisms based on time series analysis,including autocorrelation analysis,seasonal analysis,and fast Fourier transform,are designed and implemented.Threshold detection mechanisms based on function and logic operations,as well as non-seasonal anomaly detection mechanisms based on ARIMA and Holt-Winters models,and seasonal anomaly detection mechanisms based on SARIMAX and Holt-Winters models,are developed according to the characteristics of different monitoring items,significantly improving the accuracy of anomaly detection.(2)A series of monitoring and alarm strategies are formulated and refined,including delayed alarms,alarm prioritization,alarm escalation,alarm deduplication,and multi-client alarms,effectively improving the efficiency of alarm generation and preventing alarm storms.(3)IT asset scanning and information collection functionalities are implemented based on SNMP and TCP protocols,and the automation of monitoring software deployment is achieved using the Ansible tool,enhancing the automation capabilities of the monitoring system.(4)The monitoring system is visualized using the Vue framework and related components such as Element-UI and Echarts.The effectiveness of the proposed anomaly detection mechanism and the entire system is validated through experiments.Firstly,the effectiveness of the algorithm decision tree in white noise analysis and seasonal analysis is verified.Secondly,the validity of the non-seasonal and seasonal anomaly detection mechanisms is tested using real monitoring item time series data.The experiments demonstrate that the hybrid models exhibit strong robustness,enabling fitting and prediction of time series data with better performance than single models.Finally,the monitoring system is deployed and tested in a physical environment,verifying its functionalities,including asset scanning,information collection,software deployment,anomaly detection,and alarm generation.The experimental results demonstrate that the developed monitoring system performs well in terms of effectiveness and practicality,with potential for further promotion and application.