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| PROACTIVE API PERFORMANCE ANALYSER |
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Author Name Dr. K. P Malarkodi and Vishali G Abstract Modern distributed applications rely heavily on Application Programming Interfaces (APIs) to enable communication between microservices, cloud platforms, and client applications. As API usage scales, performance degradation such as latency spikes, error bursts, and throughput drops becomes inevitable. Conventional monitoring systems operate reactively, identifying failures only after threshold violations occur. This paper presents a Proactive API Performance Analyzer using Machine Learning, an intelligent monitoring framework designed to predict performance degradation and detect anomalies before they impact users. The proposed system integrates regression-based prediction models and anomaly detection algorithms within a Flask-based backend architecture. Historical and real-time API metrics such as response time, error rate, request volume, CPU usage, and memory consumption are analyzed to forecast potential failures. The system provides real-time visualization, performance risk scoring, anomaly alerts, and downloadable reports through a web-based dashboard. Experimental validation demonstrates improved reliability, reduced false alerts, and enhanced scalability compared to traditional threshold-based monitoring systems. The proposed solution supports proactive DevOps strategies and contributes toward intelligent API management in modern cloud-native environments. In large-scale enterprise environments, API performance directly influences customer satisfaction, service-level agreements
(SLAs), and revenue generation. Even minor latency increases can significantly affect user engagement and transaction completion rates. As organizations increasingly adopt DevOps and continuous deployment practices, APIs undergo frequent updates, making performance stability more complex to maintain. Therefore, intelligent monitoring mechanisms are essential to ensure uninterrupted service delivery. Furthermore, modern applications operate in highly dynamic environments where traffic patterns fluctuate due to seasonal demand, marketing campaigns, or sudden user spikes. Static monitoring configurations cannot effectively adapt to these rapid changes. An intelligent system capable of learning from historical behavior and adjusting to real-time conditions is necessary to maintain operational resilience. Predictive analytics enables early identification of degradation trends, allowing corrective measures before critical failures occur. The integration of Machine Learning into API performance management not only enhances detection accuracy but also supports data-driven decision-making. By leveraging statistical learning models, the system can uncover hidden correlations between performance metrics and system resource utilization. This holistic analytical approach strengthens infrastructure planning, capacity optimization, and long-term reliability. Consequently, proactive monitoring becomes a strategic asset rather than merely a troubleshooting tool.
Keywords : API Performance Monitoring, Machine Learning, Predictive Analytics, Anomaly Detection, Proactive Monitoring, Regression Models, Flask Framework, Data Visualization, System Reliability, DevOps Automation. Published On : 2026-03-07 Article Download : 
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