A challenge when tuning a Kubernetes microservices application is identifying the container size (CPU and Memory), due to frequent application changes and varying traffic patterns. Kubernetes autoscalers are the standard solution to automatically adjust Kubernetes container resources for service efficiency. We present the results of an extensive tuning activity we successfully conducted on a Kubernetes application delivering business-critical financial services to SMB customers. Our goal was to minimize cloud cost without compromising on performance of this application. The unexpected result was that configurations minimizing the service cost were not recommended by the autoscaler. Indeed, autoscalers work by adjusting resource sizing wrt the historical usage, without being aware of the actual cost of cloud resources and of the impact on application performance. In our session, we illustrate how you can use our exploratory testing approach we leveraged to get these results.
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