A method for managing computer block cloud volume storage for a computer application, the method comprising: calculating an optimal required cloud volume block storage capacity based on the storage needs of the application; provisioning cloud volume block storage of the optimal capacity, wherein cloud volume block storage comprises a plurality of storage blocks associated with at least one virtual block storage volume, wherein the plurality of storage blocks comprises at least one storage block of a first capacity, and at least one storage block of a capacity different to the first capacity, and wherein the at least one virtual block storage volume is distributed over one or more physical disks; receiving at least one cloud volume block storage usage metric of the application; using a reinforcement learning based predictive model, trained on historic data of at least one application, to identify at least one future time at which a cloud volume block storage capacity adjustment is required; predicting, by the reinforcement learning based predictive model, an application programming interface "API" call response time of at least one cloud volume block storage provider to fulfil a request for cloud volume block storage capacity adjustment at a specific datacenter; and adjusting the cloud volume block storage capacity within a time of the future time at which the cloud volume block storage capacity adjustment is required, wherein the cloud volume block storage capacity adjustment comprises removing surplus cloud volume block storage starting with the storage block containing the least stored data based on a file system analysis of the application indicating which storage blocks contain data which is no longer required. calculating an optimal required cloud volume block storage capacity based on the storage needs of the application; provisioning cloud volume block storage of the optimal capacity, wherein cloud volume block storage comprises a plurality of storage blocks associated with at least one virtual block storage volume, wherein the plurality of storage blocks comprises at least one storage block of a first capacity, and at least one storage block of a capacity different to the first capacity, and wherein the at least one virtual block storage volume is distributed over one or more physical disks; receiving at least one cloud volume block storage usage metric of the application; using a reinforcement learning based predictive model, trained on historic data of at least one application, to identify at least one future time at which a cloud volume block storage capacity adjustment is required; predicting, by the reinforcement learning based predictive model, an application programming interface "API" call response time of at least one cloud volume block storage provider to fulfil a request for cloud volume block storage capacity adjustment at a specific datacenter; and adjusting the cloud volume block storage capacity within a time of the future time at which the cloud volume block storage capacity adjustment is required, wherein the cloud volume block storage capacity adjustment comprises removing surplus cloud volume block storage starting with the storage block containing the least stored data based on a file system analysis of the application indicating which storage blocks contain data which is no longer required.