Detecting Anomalies in Application Performance Management System with Machine Learning Algorihms

Application performance management which is short for APM is a real-time monitoring of enterprise systems to implement a systematic solution to application performance management and fault management. APM is a relatively new network management direction. It mainly refers to monitoring and optimizing key business applications of enterprises, improving the reliability and quality of enterprise applications, ensuring users to get good services and reducing total cost of ownership (TCO). If a company's business-critical applications are powerful, they can increase competitiveness and achieve business success. Therefore, enhancing application performance management (APM) can generate huge business benefits. This paper explores the application of machine learning-based anomaly detection algorithm in APM system by analyzing continuous time series data from actual network systems.