Abstract: The present approach relates to systems and methods for facilitating run time predictions for cloud-computing automated tasks (e.g., automated tasks), and using the predicted run time to schedule resource locking. A predictive model may predict the automated task run time based on historical run time to completion, and the run time may be updated using machine learning. Resource lock schedules may be determined for a queue of automated tasks utilizing the resource based on the predicted run time for the various types of automated tasks. The predicted run time may be used to reserve a resource for the given duration, such that the resource is not available for use for another task.

Abstract: The present disclosure includes a feedback framework that receives feedback for a component of an information technology platform. The component includes the feedback framework, the information technology platform, a software application, a web browser, a client device, a client instance, or a virtual server. The feedback framework obtains context information associated with the feedback. The context information includes a system log, a screenshot, a web address of a web browser of the client device, version information, and/or the like. The feedback framework also determines an identity of the component by executing a handler. The feedback framework then determines a notification to send based on the identity of the component, and sends the notification with the feedback and the context information.

Abstract: The present approach relates to systems and methods for facilitating run time predictions for cloud-computing automated tasks (e.g., automated tasks), and using the predicted run time to schedule resource locking. A predictive model may predict the automated task run time based on historical run time to completion, and the run time may be updated using machine learning. Resource lock schedules may be determined for a queue of automated tasks utilizing the resource based on the predicted run time for the various types of automated tasks. The predicted run time may be used to reserve a resource for the given duration, such that the resource is not available for use for another task.

Abstract: The present disclosure includes a feedback framework that receives feedback for a component of an information technology platform. The component includes the feedback framework, the information technology platform, a software application, a web browser, a client device, a client instance, or a virtual server. The feedback framework obtains context information associated with the feedback. The context information includes a system log, a screenshot, a web address of a web browser of the client device, version information, and/or the like. The feedback framework also determines an identity of the component by executing a handler. The feedback framework then determines a notification to send based on the identity of the component, and sends the notification with the feedback and the context information.

Abstract: The present approach relates to systems and methods for facilitating run time predictions for cloud-computing automated tasks (e.g., automated tasks), and using the predicted run time to schedule resource locking. A predictive model may predict the automated task run time based on historical run time to completion, and the run time may be updated using machine learning. Resource lock schedules may be determined for a queue of automated tasks utilizing the resource based on the predicted run time for the various types of automated tasks. The predicted run time may be used to reserve a resource for the given duration, such that the resource is not available for use for another task.

Abstract: Provided are instance upgrade techniques useful for upgrading the software of an instance, such as in a multi-instance architecture. The techniques relate to facilitating efficient upgrades to the software of an instance by pausing workflow automations associated with the instances at non-critical stages before the upgrade begins and resuming the workflow automations after the upgrade is complete. The techniques may also include validation workflows to facilitate the upgrade and restart process.

Abstract: The present disclosure includes a feedback framework that receives feedback for a component of an information technology platform. The component includes the feedback framework, the information technology platform, a software application, a web browser, a client device, a client instance, or a virtual server. The feedback framework obtains context information associated with the feedback. The context information includes a system log, a screenshot, a web address of a web browser of the client device, version information, and/or the like. The feedback framework also determines an identity of the component by executing a handler. The feedback framework then determines a notification to send based on the identity of the component, and sends the notification with the feedback and the context information.

Abstract: The present approach relates to systems and methods for facilitating run time predictions for cloud-computing automated tasks (e.g., automated tasks), and using the predicted run time to schedule resource locking. A predictive model may predict the automated task run time based on historical run time to completion, and the run time may be updated using machine learning. Resource lock schedules may be determined for a queue of automated tasks utilizing the resource based on the predicted run time for the various types of automated tasks. The predicted run time may be used to reserve a resource for the given duration, such that the resource is not available for use for another task.

Abstract: The present disclosure includes a feedback framework that receives feedback for a component of an information technology platform. The component includes the feedback framework, the information technology platform, a software application, a web browser, a client device, a client instance, or a virtual server. The feedback framework obtains context information associated with the feedback. The context information includes a system log, a screenshot, a web address of a web browser of the client device, version information, and/or the like. The feedback framework also determines an identity of the component by executing a handler. The feedback framework then determines a notification to send based on the identity of the component, and sends the notification with the feedback and the context information.

Abstract: The present approach relates to systems and methods for facilitating run time predictions for cloud-computing automated tasks (e.g., automated tasks), and using the predicted run time to schedule resource locking. A predictive model may predict the automated task run time based on historical run time to completion, and the run time may be updated using machine learning. Resource lock schedules may be determined for a queue of automated tasks utilizing the resource based on the predicted run time for the various types of automated tasks. The predicted run time may be used to reserve a resource for the given duration, such that the resource is not available for use for another task.

Abstract: Provided are instance upgrade techniques useful for upgrading the software of an instance, such as in a multi-instance architecture. The techniques relate to facilitating efficient upgrades to the software of an instance by pausing workflow automations associated with the instances at non-critical stages before the upgrade begins and resuming the workflow automations after the upgrade is complete. The techniques may also include validation workflows to facilitate the upgrade and restart process.

Abstract: A system and method for improved software servicing by installation of an updated version is provided. Using branch versioning techniques that support multi-branch development and release versioning techniques that support multi-version development, an update package may include updated files for each branch or release version. The least possible version from the update package may be installed to minimize the risk for potential software bugs introduced by the high frequency of changes made to a higher version. Additionally, any fix installed on a target machine running a particular version may be migrated when updating the target machine to a new version without losing the fix and regressing the change. Such an update may be uninstalled by reverse migration of any fixes before uninstalling files from the version update.