Improve Cloud-Native Downtime Predictions with Intelligent Microservices Monitoring and AI-Based Log Anomaly Detection
Keywords:
microservices, AI-based monitoring, log anomaly detection, downtime predictionAbstract
Most cloud-native systems use microservices, making maintenance and monitoring tougher. Traditional monitoring systems seldom identify abnormalities and forecast downtime, causing service outages and user experience degradation. These studies examine how AI models can identify log irregularities and anticipate microservices downtime. Abnormal log entries may indicate problems, therefore we use supervised and unsupervised machine learning (ML). Artificial intelligence-driven log analysis may increase system performance, downtime prediction, and operating expenses. We solve microservices AI-based anomaly detection problems and improve model accuracy, scalability, and efficiency. Lastly, the article discusses future privacy monitoring options like federated learning.
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