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This article documents two of the most common performance tuning settings for Veeam Kasten for Kubernetes. Veeam Kasten for Kubernetes uses helm parameters, which can be tuned to adjust performance. The default settings should suit most environments. However, in complex environments, administrators may want to tune the execution control parameters to improve performance and solve various execution issues related to system resource utilization. This guide will cover the most frequent cases seen in complex environments. Note: When tuning the system, it is important to rely on the metrics Veeam Kasten for Kubernetes exposes. For many performance-related metrics, graphs are visible in the bundled Grafana instance. Make sure that Grafana and Prometheus are enabled (they are enabled by default).
Scenario 1: Veeam Kasten for Kubernetes takes a long time to pick up an action Possible cause Not enough workers to pick up actions new actions due to existing K10 worker load. Related Helm settings services.executor.workerCount executorReplicas Possible solutions It is possible to tune services.executor.workerCount to increase the number of workers per executor instance It is also possible to tune executorReplicas to increase the number of executor instances Both approaches have their pros and cons. Increasing the number of workers per executor instance will not increase the resource consumption in the cluster, but at the same time may hit the limitations of the executor pod. Increasing the number of executor instances will, on the opposite, consume more resources in the cluster, but will not hit the limit inside the executor pod. Tuning Impact Analysis To understand the impact of the changes, it is recommended to check the in-built Grafana Dashboard. Using the Executor Worker Load graph, it is possible to observe the total number of workers (worker count multiplied by the number of executor instances) over the time and the worker load (how many workers are currently in use).
Additional Useful Graphs for Troubleshooting Performance Issues Execution Control | Rate Limiter - avg operation duration Can provide more information about rate limiter behavior. This can be useful over a long time period to track the trends. Many pending tasks + low duration = many operations started Many pending tasks + high duration = operations are in process for a long time
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