# US 2021/0241132 A1 · Automatically remediating storage device issues using machine learning techniques > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241132 A1 - Document ID: US20210241132A1 - Status: Published application - Kind: A1 - Application number: 16/778,792 - Filed: 2020-01-31 - Published: 2021-08-05 - Earliest claimed: 2020-01-31 - Assignees: EMC IP Holding Company LLC - Applicants: EMC IP Holding Company LLC - Inventors: Ramya Ramachandran; Davidson Devasigamony; Jai Kumar; Sean Soltysik; Alik Saring - Web page: https://patentgrep.com/patent/US20210241132A1 ## CPC - G06F 3/0605: {by facilitating the interaction with a user or administrator} - G06N 5/04: Inference or reasoning models - G06F 3/0614: {Improving the reliability of storage systems} - G06F 3/0629: {Configuration or reconfiguration of storage systems} - G06F 3/0653: {Monitoring storage devices or systems} - G06F 3/067: {Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]} - G06N 20/00: Machine learning - G06F 11/0793: {Remedial or corrective actions (recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40)} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Methods, apparatus, and processor-readable storage media for automatically remediating storage device issues using machine learning techniques are provided herein. An example computer-implemented method includes obtaining performance-related information pertaining to one or more storage devices; automatically determining one or more workflows to be executed in connection with remediating at least one issue attributed to at least a portion of the one or more storage devices, wherein determining the one or more workflows comprises applying one or more machine learning techniques to the obtained performance-related information; and performing one or more automated actions based at least in part on the one or more workflows. ## Claims ### Claim 1 A computer-implemented method comprising: obtaining performance-related information pertaining to one or more storage devices; automatically determining one or more workflows to be executed in connection with remediating at least one issue attributed to at least a portion of the one or more storage devices, wherein determining the one or more workflows comprises applying one or more machine learning techniques to the obtained performance-related information; and performing one or more automated actions based at least in part on the one or more workflows; wherein the method is performed by at least one processing device comprising a processor coupled to a memory. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein determining the one or more workflows comprises comparing at least a portion of the obtained performance-related information to a dynamic set of configuration information pertaining to multiple storage device remediation workflows. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein comparing the at least a portion of the obtained performance-related information to the dynamic set of configuration information comprises utilizing at least one lookup table. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein performing the one or more automated actions comprises automatically outputting an identification of the one or more workflows to at least one provisioning tool associated with the one or more storage devices. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein performing the one or more automated actions comprises automatically outputting an identification of the one or more workflows to at least one user associated with the one or more storage devices for execution approval. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein performing the one or more automated actions comprises automatically outputting an identification of the one or more workflows and an indication of user approval of the one or more workflows to at least one provisioning tool associated with the one or more storage devices. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the performance-related information identifies at least one performance-related event reported by a storage device monitoring tool. ### Claim 8 (depends on 7) The computer-implemented method of claim 7, wherein determining one or more workflows to be executed in connection with remediating at least one issue comprises determining one or more workflows to be executed in connection with reactively remediating the at least one performance-related event reported by a storage device monitoring tool. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the performance-related information comprises a prediction of at least one performance-related issues generated by the storage device monitoring tool. ### Claim 10 (depends on 9) The computer-implemented method of claim 9, wherein determining one or more workflows to be executed in connection with remediating at least one issue comprises determining one or more workflows to be executed in connection with proactively remediating the prediction of at least one performance-related issues generated by the storage device monitoring tool. ### Claim 11 A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device: to obtain performance-related information pertaining to one or more storage devices; to automatically determine one or more workflows to be executed in connection with remediating at least one issue attributed to at least a portion of the one or more storage devices, wherein determining the one or more workflows comprises applying one or more machine learning techniques to the obtained performance-related information; and to perform one or more automated actions based at least in part on the one or more workflows. ### Claim 12 (depends on 11) The non-transitory processor-readable storage medium of claim 11, wherein determining the one or more workflows comprises comparing at least a portion of the obtained performance-related information to a dynamic set of configuration information pertaining to multiple storage device remediation workflows. ### Claim 13 (depends on 12) The non-transitory processor-readable storage medium of claim 12, wherein comparing the at least a portion of the obtained performance-related information to the dynamic set of configuration information comprises utilizing at least one lookup table. ### Claim 14 (depends on 11) The non-transitory processor-readable storage medium of claim 11, wherein performing the one or more automated actions comprises automatically outputting an identification of the one or more workflows to at least one provisioning tool associated with the one or more storage devices. ### Claim 15 (depends on 11) The non-transitory processor-readable storage medium of claim 11, wherein performing the one or more automated actions comprises automatically outputting an identification of the one or more workflows to at least one user associated with the one or more storage devices for execution approval. ### Claim 16 An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured: to obtain performance-related information pertaining to one or more storage devices; to automatically determine one or more workflows to be executed in connection with remediating at least one issue attributed to at least a portion of the one or more storage devices, wherein determining the one or more workflows comprises applying one or more machine learning techniques to the obtained performance-related information; and to perform one or more automated actions based at least in part on the one or more workflows. ### Claim 17 (depends on 16) The apparatus of claim 16, wherein determining the one or more workflows comprises comparing at least a portion of the obtained performance-related information to a dynamic set of configuration information pertaining to multiple storage device remediation workflows. ### Claim 18 (depends on 17) The apparatus of claim 17, wherein comparing the at least a portion of the obtained performance-related information to the dynamic set of configuration information comprises utilizing at least one lookup table. ### Claim 19 (depends on 16) The apparatus of claim 16, wherein performing the one or more automated actions comprises automatically outputting an identification of the one or more workflows to at least one provisioning tool associated with the one or more storage devices. ### Claim 20 (depends on 16) The apparatus of claim 16, wherein performing the one or more automated actions comprises automatically outputting an identification of the one or more workflows to at least one user associated with the one or more storage devices for execution approval.