Performance Benchmarking for Resource Allocation Optimization in GeoNode Ecosystems on Kubernetes Clouds

Spatial Data Infrastructures (SDI) are facing constantly growing requirements regarding number ofusers, amount of datasets and size of the raw data. Therefore, for some SDIs moving towards the cloud can be an option to improve availability. Nevertheless, cloud environments like Kubernetes are mostly more expensive than traditional in house deployments like bare metal or docker compose installation. Therefore, minimizing the required resources of a cloud hosted SDI can reduce cost while benefiting from cloud services. In this work we evaluate the resource usage for generic use cases using load testing with Locust for the open Source Spatial Data Infrastructure GeoNode deployed on Kubernetes.