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A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.

FIELD

The field relates generally to information processing systems, and more particularly to techniques for software management in such systems.

BACKGROUND

Sets or networks of user devices commonly host a multitude of software that can include operating systems (OS), device drivers, system software, application software, etc., and portions of such software often work in tandem to provide satisfactory performance. Issues among such software including configuration mismatches, lack of patch management and/or updates, software version discrepancies, etc., can cause detrimental effects on device behavior(s), introduce security and vulnerability risks, and/or produce potential outages.

However, conventional device management techniques typically include manual reactive actions taken in response to user communications, often requiring time-consuming and error-prone troubleshooting efforts that can be duplicative across multiple devices.

SUMMARY

Illustrative embodiments of the disclosure provide techniques for automatically determining configuration-based issue resolutions across multiple devices using machine learning models. An exemplary computer-implemented method includes training, using historical data related to device information and device configuration information from a set of devices, multiple machine learning models comprising: at least a first machine learning model configured to determine one or more device issues and one or more corresponding device issue resolutions, and at least a second machine learning model configured to identify similar devices within the set of devices. The method also includes determining, in connection with input data associated with a given device from the set of devices, at least one device issue and at least one corresponding device issue resolution, by processing at least a portion of the input data using the at least a first machine learning model. Further, the method additionally includes identifying one or more additional devices within the set of devices that are similar to the given device by processing at least a portion of the input data using the at least a second machine learning model, and performing, based at least in part on the at least one determined device issue resolution, one or more automated actions in connection with the given device and at least a portion of the one or more identified additional devices.

Illustrative embodiments can provide significant advantages relative to conventional device management techniques. For example, problems associated with duplicative and error-prone manual troubleshooting efforts are overcome in one or more embodiments through automatically determining configuration-based issue resolutions across multiple devices using machine learning models.

These and other illustrative embodiments described herein include, without limitation, methods, apparatus, systems, and computer program products comprising processor-readable storage media.

