Abstract: A method for managing upgrades of components of clients includes obtaining an upgrade failure prediction request associated with a client of the clients, and in response to obtaining an update failure prediction request: obtaining live data associated with the client, matching the live data with a training data cluster, selecting relevant features associated with processed training data of the training data cluster, generating an upgrade failure prediction using the live data associated with the relevant features and a prediction model, making a determination that the upgrade failure prediction implicates an action is required, and based on the determination, initiating performance of the action.

Abstract: A method comprises receiving a notification of an issue with at least one component of a plurality of components in a computing environment. One or more machine learning algorithms are used to determine one or more components of the plurality of components impacted by the issue with the at least one component. The method further comprises collecting operational data for the at least one component and the one or more impacted components.

Abstract: A method for managing applications includes obtaining, by a client in a customer environment, an upgrade issue report for the application, making a first determination that a resynchronization of a client self-healing classification model with the production host environment (PHE) self-healing classification model is required, wherein the PHE self-healing classification model is stored in the PHE, performing the resynchronization with the PHE self-healing classification model to obtain a synchronized client self-healing classification model, applying the synchronized client self-healing classification model to the upgrade issue report to obtain a state of the upgrade issue report, making a second determination that the state indicates a self-healable state, based on the second determination, performing a self-healing process on the application based on the upgrade issue report, and storing a resolution report based on results of the self-healing process, wherein the PHE is operatively connected to the custome

Abstract: A method for managing applications includes obtaining, by a client in a customer environment, an upgrade issue report for the application, making a first determination that a resynchronization of a client self-healing classification model with the production host environment (PHE) self-healing classification model is required, wherein the PHE self-healing classification model is stored in the PHE, performing the resynchronization with the PHE self-healing classification model to obtain a synchronized client self-healing classification model, applying the synchronized client self-healing classification model to the upgrade issue report to obtain a state of the upgrade issue report, making a second determination that the state indicates a self-healable state, based on the second determination, performing a self-healing process on the application based on the upgrade issue report, and storing a resolution report based on results of the self-healing process, wherein the PHE is operatively connected to the custome

Abstract: A method for managing upgrades of components of clients includes obtaining an upgrade failure prediction request associated with a client of the clients, and in response to obtaining an update failure prediction request: obtaining live data associated with the client, matching the live data with a training data cluster, selecting relevant features associated with processed training data of the training data cluster, generating an upgrade failure prediction using the live data associated with the relevant features and a prediction model, making a determination that the upgrade failure prediction implicates an action is required, and based on the determination, initiating performance of the action.

Abstract: A method for managing applications in a production host environment includes obtaining, by an application upgrade management system, an upgrade issue report for the application, applying a self-healing classification model to the upgrade issue report to obtain a state of the upgrade issue report, making a determination that the state indicates a self-healable state, based on the determination, performing a self-healing process on the application based on the upgrade issue report, and storing a resolution report based on results of the self-healing process.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving an input to register a device for tracking, the input comprising registration data specifying the device and one or more components in the device to register for tracking and a minimum acceptance criterion for each of the one or more components, the minimum acceptance criterion for a registered component defines a minimum threshold percentage of votes needed to approve a modification to the registered component. The method also includes recording the device identifier and the registration data related to the device in a device ledger, generating a rule execution set for the device ledger, the rule execution set comprising one or more rules pertaining to approval of requests to modify the device, and processing requests to modify the device or the one or more registered components in the device based on the specified minimum acceptance criterion and the rule execution set.

Abstract: A method comprises receiving a notification of an issue with at least one component of a plurality of components in a computing environment. One or more machine learning algorithms are used to determine one or more components of the plurality of components impacted by the issue with the at least one component. The method further comprises collecting operational data for the at least one component and the one or more impacted components.