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Version: 1.3.0


Kubernetes metrics provide valuable insights into the performance and health of your Kubernetes clusters and workloads. These metrics encompass various aspects such as resource utilization, network traffic, and application-specific metrics. By collecting and analyzing Kubernetes metrics, you can gain a deep understanding of your cluster's behavior, identify bottlenecks, optimize resource allocation, and make data-driven decisions for scaling and performance tuning. Monitoring Kubernetes metrics is crucial for maintaining the stability, efficiency, and reliability of your containerized environment.

📄️ Slice Operator Metrics

The Slice Operator metrics provide valuable insights into the performance and health of various components within the KubeSlice environment. These metrics are categorized based on different labels associated with each component. The metrics include information such as the number of active endpoints in service imports and exports, the number of application pods in each slice per namespace, the health status of the worker cluster and its components, the health status of individual slices and their components, and counters for slice-related events. These metrics help you monitor the state of your KubeSlice deployment and make informed decisions for efficient management. The metrics are accompanied by labels that provide additional context, such as the action triggering the metric, the component or object involved, the project and cluster names, and more. By leveraging these metrics and labels, you can effectively monitor and optimize your KubeSlice infrastructure.