Benjamin T Randall

20190311293, Oct 10, 2019

Jun 23, 2019

16/449418

- Armonk NY, US
Andrew J. Laforteza - Austin TX, US
Ryan D. Mcnair - Austin TX, US
Benjamin J. Randall - Fort Collins CO, US
Teresa S. Swingler - Tucson AZ, US

G06N 20/00
G06F 3/06

A method, system and computer product for performing storage maintenance is described. A training set for storage volume reclamation is received. The training set for storage volume reclamation contains sets of storage parameters for storage volumes and corresponding user decisions whether the storage volumes are reclaimable. The training set is used to train a machine learning system to recognize reclaimable candidate storage volumes. The trained machine learning system is used to determine that a candidate storage volume for reclamation is likely a reclaimable storage volume.

20170351968, Dec 7, 2017

Jun 3, 2016

15/173562

- Armonk NY, US
Andrew J. Laforteza - Austin TX, US
Ryan D. Mcnair - Austin TX, US
Benjamin J. Randall - Fort Collins CO, US
Teresa S. Swingler - Tucson AZ, US

G06N 99/00
G06F 3/0481
G06F 3/06

A method, system and computer product for performing storage maintenance is described. A training set for storage volume reclamation is received. The training set includes a set of storage parameters, each set of storage parameters corresponds to a respective candidate storage volume of a set of candidate storage volumes. The training set also includes a set of user decisions made whether a respective candidate storage volume is reclaimable. The training set is used to train a machine learning system to recognize common features of reclaimable candidate storage volumes. A set of candidate storage volumes is provided for potential reclamation, each with a set of storage parameters. A graphical user interface presents respective members of the set of candidate storage volumes for reclamation if a confidence level is calculated that the respective candidate storage volume is reclaimable exceeds a threshold.

20160299711, Oct 13, 2016

Apr 10, 2015

14/683677

- ARMONK NY, US
Stephen A. Byrd - Sunnyvale CA, US
Benjamin J. Randall - Fort Collins CO, US

G06F 3/06

A method, computer program product, and system for determining an optimal volume placement on a storage system. The method includes acquiring at least a desired response time range for each storage volume associated with an application. The degree of optimization of volume placement is evaluated utilizing numeric scores assigned by a fitness function, and a recommendation is provided for storage volumes that should be moved to different storage pools, such that desired response times are met.

20140359243, Dec 4, 2014

May 31, 2013

13/906746

- Armonk NY, US
Benjamin J. Randall - Fort Collins CO, US

G06F 3/06

711170

A mechanism is provided in a data processing system for volume placement across remote replication relationships. Responsive to applying a volume placement optimization to a source storage pool having at least one volume with a replication target in a target storage pool, the mechanism determines whether the source storage pool and the target storage pool have identical storage volume membership. Responsive to determining the source storage pool and the target storage pool not having the same storage volume membership, the mechanism generates workload performance data for each non-shared storage volume in the target storage pool that does not have a corresponding member of the source storage pool. The mechanism generates a hardware profile for hardware of the target storage pool. The mechanism generates a volume placement optimization for the storage volumes in the target storage pool based on the workload performance data for each non-shared storage volume in the target storage pool and the hardware profile for the hardware of the target storage pool.