REVOLUTIONIZING APPLICATION PERFORMANCE MONITORING: LEVERAGING MACHINE LEARNING TO MITIGATE FALSE ALERTS AND ENHANCE PREDICTIVE CAPABILITIES IN COMPLEX DISTRIBUTED SYSTEMS

Abstract

In today's rapidly evolving technological landscape, the need for swift and efficient application troubleshooting has become increasingly critical. Recent disruptions, such as the brief Microsoft outage that halted global activities, underscore the urgency for advanced solutions in Application Performance Monitoring (APM). Traditional APM methods, often rule-based and manual, struggle to cope with the complexities of modern distributed systems, leading to issues like excessive false alerts and inefficient resource allocation. In this context, Artificial Intelligence (AI) emerges as a vital solution, offering superior anomaly management and troubleshooting capabilities compared to traditional methods. The limitations of human intervention become apparent as operational scales and complexity increase. Whether it involves microsystems or expansive distributed systems, AI can navigate both hardware and software challenges effortlessly, transforming intricate monitoring tasks into manageable operations. This research paper delves into the integration of Machine Learning (ML) with APM frameworks to surmount these obstacles. It proposes innovative ML-driven strategies for refined anomaly detection, advanced predictive analytics, and sophisticated automated decision-making processes. Leveraging ML, APM tools can significantly improve in precision, efficiency, and adaptability, thereby boosting overall application performance and ensuring a more consistent user experience.