Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.