An approach for automatic storage planning and provisioning within a clustered computing environment is provided. Planning input for a set of storage area network volume controllers (SVCs) will be received within the clustered computing environment, the planning input indicating a potential load on the SVCs and its associated components. Analytical models (e.g., from vendors) can be also used that allow for a load to be accurately estimated on the storage components. Configuration data for a set of storage components (i.e., the set of SVCs, a set of managed disk (Mdisk) groups associated with the set of SVCs, and a set of backend storage systems) will also be collected. Based on this configuration data, the set of storage components will be filtered to identify candidate storage components capable of addressing the potential load. Then, performance data for the candidate storage components will be analyzed to identify an SVC and an Mdisk group to address the potential load.