Abstract
The present disclosure provides a machine learning platform to automatically perform patching of servers at internet scale. The present disclosure discloses a patching automation engine for initiating, tracking, and recording patching activities. Users provide patching workflows and validation rules for performing the patching, which is executed by the patching automation engine. Since the patching automation engine performs logistic activities, the users can keep on adding behavior and rules. This simplifies the diversity in software patching across organizations and supporting different behaviors based on requirements of the users. The system or the patching automation engine uses user-based workflows in a streamlined manner, accounting for administrator and cluster-specific requirements. The system improves existing workflow by facilitating users and admins to maintain version-controlled patching workflows that are independent of each other, while internally implementing checks and balances.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
THOTA, HEMANTH; CHANDRA, SAURABH; MEHROTRA, RISHU; and KEERTHI, VINAY, "MACHINE LEARNING PLATFORM TO AUTOMATICALLY PERFORM PATCHING OF SERVERS", Technical Disclosure Commons, (June 05, 2023)
https://www.tdcommons.org/dpubs_series/5938