Abstract: Methods, systems, and computer-readable storage media for receiving, by a self-healing platform within the container orchestration system, fault data that is representative of two or more error events occurring within a cluster provisioned within the container orchestration system, determining, by the self-healing platform, a set of actions to be executed in response to the two or more error events, providing, by the self-healing platform, a priority value for each error event of the two or more error events, and transmitting, by the self-healing platform, instructions to execute actions in the set of actions based on respective priority values of the two or more error events.

Abstract: A system and method of automating fault prediction and remediation for a multi-tenant system is disclosed. The system and method offer an intelligent augmentation of a multi-tenant system by automating the harvesting and processing of raw data logs generated by the various aspects of the platform as well as the implementation of an appropriate response. In some embodiments, the proposed system includes a hybrid model that can be configured to offer both assisted and unassisted errors. The incorporation of a dynamic learning algorithm minimizes operation errors for any set of computing units. Potential system faults can be detected with little to no human intervention and allow for an unattended platform that collects performance data across the system from a wide range of sources to provide centralized and automated fault prediction, as well as expedited automated resolutions to such faults that depend on little to no human intervention.

Abstract: A system and method of automating fault prediction and remediation for a multi-tenant system is disclosed. The system and method offer an intelligent augmentation of a multi-tenant system by automating the harvesting and processing of raw data logs generated by the various aspects of the platform as well as the implementation of an appropriate response. In some embodiments, the proposed system includes a hybrid model that can be configured to offer both assisted and unassisted errors. The incorporation of a dynamic learning algorithm minimizes operation errors for any set of computing units. Potential system faults can be detected with little to no human intervention and allow for an unattended platform that collects performance data across the system from a wide range of sources to provide centralized and automated fault prediction, as well as expedited automated resolutions to such faults that depend on little to no human intervention.