A system for container migration includes containers running instances of an application running on a cluster, an orchestrator with a controller, a memory, and a processor in communication with the memory. The processor executes to monitor a vitality metric of the application. The vitality metric indicates that the application is in either a live state or a dead state. Additionally, horizontal scaling for the application is disabled and the application is scaled-down until the vitality metric indicates that the application is in the dead state. Responsive to the vitality metric indicating that the application is in the dead state, the application is scaled-up until the vitality metric indicates that the application is in the live state. Also, responsive to the vitality metric indication transitioning from the dead state to the live state, the application is migrated to a different cluster while the horizontal scaling of the application is disabled.

Aspects of the present disclosure involve a system and method for performing operations comprising providing to a client device, a messaging application comprising multiple features; accessing a configuration rule that associates a device property rule with a feature; determining at a first point in time, that a property of the client device matches the device property rule associated with the configuration rule; in response to determining that the property of the client device matches the device property rule associated with the configuration rule, enabling the feature on the client device at the first point in time; receiving an updated property of the client device at a second point in time; and in response to determining that the updated property of the client device fails to match the device property rule associated with the configuration rule at the second point in time, disabling the feature on the client device.

Described herein are example systems and computer-implemented methods for monitoring changes to an application. For example, information regarding a change made to an aspect of an application may be received by a processor. It, may be determined if a similarity of the change to a cluster of changes related to the aspect is within a change threshold. Further, the change may be associated with the cluster of changes when the similarity of the change is within the change threshold. It may be further determined if a metric based on a number of changes associated with the cluster of changes is within a cluster range. When the metric within the cluster range, a prototype change may be extracted from the cluster of changes. The application may be updated based on the prototype change when the metric is within the cluster range.

A processing unit can include a performance monitor for monitoring the performance of the processing unit and associated sub-units. The performance monitor can include a state machine. The state machine can be implemented via state machine data entries stored in a memory associated with the performance monitor. A state machine data entry includes information indicating a state transition condition and output signals. The state transition condition includes a current state and input signals required to meet the condition. The output signals include a next state, one or more counter actions, and one or more triggers. The performance monitor implements logic circuits that determine, based on input signals and the state machine data entries, the next state to transition and associated output signals. The state machine data entries can be written and re-written by a user.

Disclosed herein are embodiments for managing a task including one or more skills. A server stores a virtual environment, software agents configured to collect data generated when a user interacts with the virtual environment to perform the task, and a predictive machine learning model. The server generates virtual entities during the performance of the task, and executes the predictive machine learning model to configure the virtual entities based upon data generated when the user interacts with the virtual environment. The server generates the virtual environment and the virtual entities configured for interaction with the user during display by the client device, and receives the data collected by the software agents. The system displays a user interface at the client device to indicate a measurement of each of the skills during performance of the task. The server trains the predictive machine learning model using this measurement of skills during task performance.

A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: monitoring data center assets within a data center; identifying an issue within the data center, the issue being associated with an operational situation associated with a particular component of the data center; determining whether data associated with the issue corresponds to predefined conditional criteria; and, triggering an autonomous widget operation in response to a determination of the data associated with the issue corresponding to the predefined conditional criteria, the autonomous widget operation executing a particular autonomous widget.

A quality score for a computer application release is determined using a first number of unique users who have launched the computer application release on user devices and a second number of unique users who have encountered at least once an abnormal termination with the computer application release on user devices. Additionally or optionally, an application quality score can be computed for a computer application based on quality scores of computer application releases that represent different versions of the computer application. Additionally or optionally, a weighted application quality score can be computed for a computer application by further taking into consideration the average application quality score and popularity of a plurality of computer applications.

Techniques described herein relate to a method for managing telemetry services for composed information handling systems. The method includes obtaining, by a system control processor manager, a telemetry request associated with a composed information handling system from a user; in response to obtaining the telemetry request: identifying a transaction identifier associated with the telemetry request; identifying telemetry intent associated with the telemetry request; aggregating telemetry data based on the telemetry intent and a telemetry data map entry associated with the transaction identifier to obtain aggregated telemetry data; and providing the aggregated telemetry data to the user.

Methods and systems for a media guidance application that provides advanced parental control features such as allowing parents to establish parental controls in a dynamic and individualized manner and allowing parents to track and/or limit the amount of time that a child views media content of a particular type.

In accordance with an embodiment, described herein is a system and method for use in a distributed computing environment, for automatically scaling a cluster based on metrics being monitored. A cluster that comprises a plurality of nodes or brokers and supports one or more colocated partitions across the nodes, can be associated with an exporter process and alert manager that monitors metrics associated with the cluster. Various metrics can be associated with user-configured alerts that trigger or otherwise indicate the cluster should be scaled. When a particular alert is raised, a callback handler associated with the cluster, for example an operator, can automatically bring up one or more new nodes, that are added to the cluster, and then reassign a selection of existing colocated partitions to the new nodes/brokers, such that computational load can be distributed within the newly-scaled cluster environment.