Emc IP Holding Company LLC

Dell Products Inc.

Deepak Gowda

Bina K Thakkar

Aditya Krishnan

Sathish Kumar Bikumala

Chao Su

Roopa A Luktuke

Wenjin Liu

Shenee Prakash Ashara

Parminder Singh Sethi

Sudhir Vittal Shetty

Vinay Sawal

Anne-Marie McReynolds

Ramya Ramachandran

Shou-Huey Jiang

Arun Rameshbabu

Francois Auger

Systems and methods for detecting data storage drive failures are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include: a processor; and a memory coupled to the processor, where the memory includes program instructions store thereon that, upon execution by the processor, cause the IHS to: obtain data attributes of a data storage drive within a system of data storage drives; generate, based at least in part on the data attributes of the data storage drive, engineered features related to the system of data storage drives; and generate, using a trained artificial intelligence or machine learning model, and based at least in part on the engineered features, a probability of failure for the data storage drive.

System and method to detect data storage drive failures

A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: receiving data center data; identifying unprotected sensitive data center data contained within the data center data; classifying the sensitive data center data contained within the data center data according to a sensitivity based classification; and, remediating the sensitive data center asset data according to the sensitivity based classification of the sensitive data center data.

Data center monitoring and management operation for discovering, analyzing and remediating sensitive data center data

A method, system, and computer-usable medium are disclosed for acquiring and using audio detected during operation of a hard disk drive to determine the status of the hard disk drive. One general aspect of the disclosure is directed to a computer-implemented method that includes receiving analog audio detected by an audio transducer in proximity to moving mechanical components of a hard disk drive, converting the received analog audio to digital audio, and assigning a health classification status to the hard disk drive based on a classification of the digital audio.

System and method for acquiring and using audio detected during operation of a hard disk drive to determine drive health

Methods, apparatus, and processor-readable storage media for determining storage system configuration recommendations based on vertical sectors and size parameters using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to multiple storage systems; segmenting, into one or more segments, the multiple storage systems based on one or more vertical sectors and one or more size parameters of an enterprise associated with each storage system; identifying, within each of the segments, each storage system running one or more vertical sector-specific applications; clustering, within each of the segments, the storage systems running one or more vertical sector-specific applications based on configuration information using at least one machine learning technique; generating one or more storage system configuration templates based on the clustering; and performing one or more automated actions based on the one or more generated storage system configuration templates.

Determining storage system configuration recommendations based on vertical sectors and size parameters using machine learning techniques

A method comprises extracting data for one or more assets corresponding to a user, and analyzing the data using one or more machine learning models. The analyzing comprises predicting whether the one or more assets will require at least one of replacement and service. In the method, one or more entitlement recommendations for the user are generated based on the analysis, and the one or more entitlement recommendations are transmitted to the user.

Automated and customized entitlement recommendation

Methods, apparatus, and processor-readable storage media for determining capacity in storage systems using machine learning techniques are provided herein. An example computer-implemented method includes obtaining capacity-related data from a storage system; forecasting, for a given temporal period, capacity of one or more storage objects of the storage system by applying machine learning techniques to at least a portion of the capacity-related data; aggregating the forecasted capacity for at least portions of the one or more storage objects; determining, based on the aggregated forecasted capacity of the storage objects, whether at least a portion of the storage system will run out of capacity in connection with the given temporal period; and performing one or more automated actions based at least in part on the determination as to whether the at least a portion of the at least one storage system will run out of capacity.

Determining capacity in storage systems using machine learning techniques

Methods, apparatus, and processor-readable storage media for implementing a machine learning-based recommendation engine for storage system usage within an enterprise are provided herein. An example computer-implemented method includes processing input data pertaining to multiple storage systems within an enterprise; determining association rules applicable to the multiple storage systems by applying machine learning techniques to the processed input data; generating configuration-related recommendations applicable to one or more of the storage systems by applying content filtering techniques to the determined association rules; and outputting, via user interfaces, the configuration-related recommendations to a user for use in connection with storage system configuration actions and/or an entity within the enterprise for use in connection with user-support actions.

Machine learning-based recommendation engine for storage system usage within an enterprise

Methods, apparatus, and processor-readable storage media for detecting security threats in storage systems using AI techniques are provided herein. An example computer-implemented method includes obtaining historical performance data and historical capacity data pertaining to one or more storage objects within a storage system; determining supervised datasets pertaining to security threat-related data and non-security threat-related data by processing at least a portion of the obtained data using a first set of AI techniques; configuring a second set of AI techniques based at least in part on the determined supervised datasets; detecting one or more security threats in connection with at least one storage object within the storage system by processing input data from the at least one storage object using the second set of AI techniques; and performing at least one automated action based at least in part on the one or more detected security threats.

Detecting security threats in storage systems using artificial intelligence techniques

A method, system, and computer-usable medium are disclosed for classifying audio detected during operation of a hard disk drive to determine the status of the hard disk drive. One general aspect of the disclosure is directed to a system in which digital audio corresponding to audio detected by an audio transducer in proximity to moving mechanical components of a hard disk drive is recorded. The recorded digital audio is classified, using a trained machine learning model, to provide a health status of the hard disk drive based on the classification.

System and method for classifying audio detected during operation of a hard disk drive to determine drive health

Methods, apparatus, and processor-readable storage media for determining user retention values using machine learning and heuristic techniques are provided herein. An example computer-implemented method includes processing multiple forms of input data pertaining to interactions between a user and an enterprise; generating one or more user sentiment values from the processed input data by applying machine learning techniques to the processed input data; determining a user-specific estimate for the enterprise retaining the user, wherein determining the user-specific estimate comprises combining the one or more sentiment values with one or more storage system heuristics-based values derived from enterprise-related data; and outputting the user-specific estimate to at least one entity within the enterprise for use in connection with user-support actions.

Determining user retention values using machine learning and heuristic techniques

Methods, apparatus, and processor-readable storage media for automatically determining sizing configurations for storage components using machine learning techniques are provided herein. An example computer-implemented method includes obtaining multiple items of input related to at least one storage component; determining a set of storage component sizing configurations by processing at least a portion of the multiple items of input using a first set of one or more machine learning techniques comprising at least one deep learning technique; identifying a subset of the storage component sizing configurations by processing at least a portion of the determined set of storage component sizing configurations using a second set of one or more machine learning techniques; and performing one or more automated actions based at least in part on the identified subset of storage component sizing configurations.

Automatically determining sizing configurations for storage components using machine learning techniques

Methods, apparatus, and processor-readable storage media for reactive storage system-based software version analysis using machine learning techniques are provided herein. An example computer-implemented method includes obtaining user service requests, each comprising a description of problems and data pertaining to storage systems associated with the requests; calculating similarity measures for the user service requests by applying a machine learning algorithm to the user service requests; automatically grouping the user service requests into a set based on the similarity measures; automatically grouping, within the set, two or more of the user service requests into subsets based on a software version attributed to the storage systems associated with the two or more user service requests; generating an output pertaining to actions related to a software version update; and transmitting the output to at least one of the users corresponding to the user service requests in at least one of the subsets.

Reactive storage system-based software version analysis using machine learning techniques

Methods, apparatus, and processor-readable storage media for proactive storage system-based software version analysis using machine learning techniques are provided herein. An example computer-implemented method includes obtaining storage system data from multiple storage systems; determining performance issues among the storage systems by applying a machine learning algorithm to the storage system data; automatically grouping the storage system data into a set of groups based on issue type among the determined performance issues; automatically grouping, within the set, the storage system data into subsets based on a software version attributed to the corresponding storage system data; generating an output pertaining to actions to be performed with respect to at least one software version update; and transmitting the output to users of the storage systems which correspond to the storage system data in at least one of the subsets.

Proactive storage system-based software version analysis using machine learning techniques

Methods, apparatus, and processor-readable storage media for classification of storage systems and users thereof using machine learning techniques are provided herein. An example computer-implemented method includes processing input data pertaining to multiple storage systems within an enterprise; classifying one or more of the storage systems by applying a first set of machine learning techniques to the processed input data; classifying one or more respective users of the classified storage systems by applying a second set of machine learning techniques to the processed input data associated with the classified storage systems; and outputting, via one or more user interfaces, at least a portion of the storage system classifications and at least a portion of the user classifications to a user for use in connection with storage system configuration actions and/or an entity within the enterprise for use in connection with user-support actions.

Growing community of inventors

Cary, NC, United States of America

Deepak Gowda