Abstract: Methods, apparatus, and processor-readable storage media for duplicate incident detection using a dynamic similarity threshold are provided herein. An example computer-implemented method includes obtaining a request including information associated with tracking at least a first incident in a database; generating a first representation of the first incident that encodes at least a portion of the information; computing a set of similarity scores for the first incident, where a given similarity score is based on a comparison between the first representation and a second representation generated for one of a plurality of additional incidents in the database; detecting that the first incident is a duplicate of at least one of the plurality of additional incidents based on a comparison of the set of similarity scores to a similarity threshold, where the similarity threshold is updated over time; and initiating an update in the database in response to the detecting.

Abstract: A method, computer program product, and computing system for processing historical input/output (IO) performance data associated with one or more storage objects of a storage system. A plurality of IO modeling systems may be trained using the historical IO performance data. Modeling performance information may be determined for the plurality of IO modeling systems across the historical IO performance data. A forecast score may be determined for each IO modeling system based on the modeling performance information for the plurality of IO modeling systems. A subset of the plurality of IO modeling systems may be selected based upon the forecast score for each IO modeling system. The at least one IO modeling system may be trained using the historical IO performance data. IO performance data may be forecasted using the at least one trained IO modeling system from the subset of the plurality of IO modeling systems.

Abstract: A method, computer program product, and computing system for processing historical input/output (IO) performance data associated with one or more storage objects of a storage system. A smoothing model may be applied on at least a portion of the historical IO performance data to generate forecast IO performance data. The forecast IO performance data may be compared to observed IO performance data to generate one or more performance differentials. A normal IO performance range may be generated based upon, at least in part, the one or more performance differentials. One or more IO performance anomalies may be detected based upon, at least in part, the normal IO performance range.

Abstract: Data storage systems of a cluster associate virtual volumes (VVols) with protocol endpoints (PEs) organized into host-specific initiator groups. From the perspective of the hosts, the PEs are specialized SCSI LUNs that aggregate the VVols bound thereto. In operation, a host directs VVol access requests to the host-specific PEs, and a data storage system processes the VVol requests as received via the corresponding PEs.

Abstract: Data storage systems of a cluster associate virtual volumes (VVols) with protocol endpoints (PEs) organized into host-specific initiator groups. From the perspective of the hosts, the PEs are specialized SCSI LUNs that aggregate the VVols bound thereto. In operation, a host directs VVol access requests to the host-specific PEs, and a data storage system processes the VVol requests as received via the corresponding PEs.