Dell Products Inc.

Emc IP Holding Company LLC

David Sydow

Avitan Gefen

Shaul Dar

Anil Kumar Koluguri

Shobhit Nitinkumar Dutia

Duhita Mulky Avinash

Jeremy Denis White

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.

Duplicate incident detection using dynamic similarity threshold

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.

System and method for modeling and forecasting input/output (IO) performance using adaptable machine learning

Techniques for grouping system logs using machine learning. The techniques include deriving an input matrix from the logs, in which rows represent the logs and columns represent unique words in the logs. The techniques include applying a TF-IDF algorithm to the input matrix and deriving a TF-IDF matrix from the TF-IDF algorithm output. The TF-IDF matrix reflects how important the unique words are to the respective logs. The techniques include applying a PCA algorithm to the TF-IDF matrix and deriving a PCA matrix having a reduced dimensionality from the PCA algorithm output. The techniques include applying a Cosine Similarity algorithm to the PCA matrix and deriving CS matrices from the CS algorithm output. Each CS matrix reflects the cosine similarity of a respective log relative to all the other logs. The techniques include applying a clustering algorithm to the CS matrices and deriving log groupings from the clustering algorithm output.

Systems and methods of grouping system logs using unsupervised machine learning

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Merrimack, NH, United States of America

David Sydow