Using Artifical Intelligence in Amazon EKS Clusters to Detect Faults Percipiently
Keywords:
Amazon EKS, AI-driven fault detection, Kubernetes-native toolsAbstract
Maintaining performance & user satisfactions in cloud-native settings such as Amazon Elastic Kubernetes Service requires ensuring high availability & reducing downtime. In order to proactively identify & stop errors in Elastic Kubernetes Service clusters, this research investigates a machine learning-based method. The system detects early indicators of problems that can cause service interruptions by keeping an eye on parameters including pod performance, node health & network conditions. These algorithm forecasts possible issues & offers automated remedies & real-time alerts based on their past performance data. Teams may fix issues before they affect consumers by examining trends in resource utilizations, latency and system faults . This method lessens operating workload while simultaneously enhancing Elastic Kubernetes Service clusters stability & dependability. The study demonstrates how performance and resilience may be improved by incorporating machine learning and AI into Kubernetes operations, providing a more creative approach to cloud infrastructure management.
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