Enhanced Kubernetes Monitoring Through Distributed Event Processing

As organizations increasingly adopt cloud-native architectures, Kubernetes has emerged as the leading container orchestration platform. However, monitoring such dynamic and distributed environments remains a significant challenge. Traditional observability tools often fall short in detecting real- time anomalies and understanding contextual issues across multiple clusters. This study introduces an enhanced Kubernetes monitoring system based on Distributed Event Processing (DEP). The proposed system integrates telemetry collection, event stream processing, and complex event correlation to offer intelligent and scalable monitoring capabilities. It enables proactive identification of issues such as pod failures, network delays, and performance bottlenecks by analyzing real-time event data from Kubernetes clusters. The architecture supports multi-cluster observability using distributed tools like Apache Kafka, Flink, Prometheus, and Fluentd. Empirical results demonstrate improved detection speed, reduced false positives, and faster recovery time, making this approach highly effective for modern DevOps workflows.