Scalable performance-based volume allocation in large storage controller collections

A scalable, performance-based, volume allocation technique that can be applied in large storage controller collections is disclosed. A global resource tree of multiple nodes representing interconnected components of a storage system is analyzed to yield gap values for each node (e.g., a bottom-up estimation). The gap value for each node is an estimate of the amount in GB of the new workload that can be allocated in the subtree of that node without exceeding the performance and space bounds at any of the nodes in that subtree. The gap values of the global resource tree are further analyzed to generate an ordered allocation list of the volumes of the storage system (e.g., a top-down selection). The volumes may be applied to a storage workload in the order of the allocation list and the gap values and list are updated.