An example operation may include a system, comprising one or more of: receiving a status failure notification for a VNFCI, retrieving a peer VNFCI admin state and a peer VNFCI operational state, taking no action when one or more of: the peer VNFCI admin state is not online, the peer VNFCI is not reachable, and the peer VNFCI operational state is active, retrieving current issues reported on resources associated with the peer VNFCI when one or more of: the peer VNFCI admin state is online, the peer VNFCI is reachable, and the peer VNFCI operational state is not active, sending a state change request message with an active state to the peer VNFCI when the current issues do not exist, and starting a retry timer for the peer VNFCI.

A streaming distributed decentralized database task system can perform multiple tasks of parallel jobs on clusters of nodes without overloading the clusters' computational resources, such as disk, memory, processors, and network bandwidth. A cluster master can manage a job and add items to node queues. A node manager accepts or rejects queue items based on streaming task limits that are applied at the node level.

A bezel configured to mechanically couple to a housing for housing components of an information handling system may include one or more mechanical features for mechanically coupling the bezel to the housing, an illuminated element module mechanically coupled to a mechanical structure of the bezel, and a bezel connector having a plurality of pins communicatively coupled to the illuminated element module, the bezel connector configured to communicatively couple the illuminated element module to an access controller of the information handling system housed within the housing, such that the illuminated element module receives instructions from the access controller for displaying a visual behavior relating to a status of the information handling system and displays the visual behavior in response to receiving the instructions.

Techniques are disclosed for managing the connection assignments of a plurality of accessory devices to one or more hub devices. In one example, a user device acting as a leader device receives an assignment request from an accessory device. The user device may obtain information corresponding to hub attributes from the one or more hub devices. The user device may also obtain accessory traits from the accessory device. The user device can compare the accessory traits with the hub attributes to determine a connection score for each hub device. The user device can then assign the accessory device to the hub device with the highest connection score.

Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.

At least one device can be classified based on user activity. At least one telemetry variable with respect to time may be received for the at least one device. The at least one telemetry variable with respect to time may be used to determine an activity model for the at least one device. Based on the activity model, a probability of the at least one device belonging to a profile or to a type of user may be determined. Based on the type of user of the at least one device, a setting associated with the at least one device may be changed.

Replicated instances in a distributed computing environment provide for automatic failover and recovery. A component monitors the status of event processors in a set or bucket and handles the failure of an event processor. For a large number of instances, the data environment can be partitioned such that each monitoring component is assigned a partition of the workload. At intervals, each event processor sends a “heartbeat” message to the event processors in the bucket covering the same workload partition, to inform the other event processors of the status of the event processor sending the heartbeat. If it is determined that a heartbeat is received from each event processor in the bucket, a current process can continue. In the event of monitoring component failure, the instances can be repartitioned, and the remaining monitoring components can be assigned to the new partitions to substantially evenly distribute the workload.

A lightweight always-on monitoring, collecting, diagnosing, and correcting utility operates in an enhanced storage manager that manages a data storage managements system. The always-on utility provides a comprehensive and pro-active approach, which is intended to reduce, if not altogether eliminate, the need for after-the-fact diagnostics. The always-on utility also enforces so-called best practices and other heuristics, which include pro-actively activating certain database settings that are not enabled by default; manipulating certain aspects of the database to improve performance; and reporting aspects that are outside best-practice parameters to the trouble report system so that system administrators and/or developers may intervene before a catastrophic failure occurs. In some cases, the best-practice parameters represent heuristics designed by the present inventors to improve the performance and general health of the management database.

A system to provide increased visibility and insight into unexpected usage patterns for electronic devices is described. The utilization analysis system applies a utilization model to device utilization information. The utilization model is also used to determine one or more device usage patterns for the electronic device. These device usage patterns are used to determine an unexpected device utilization where the system responds to the unexpected device utilization with several hardware and software solution proposals to prevent damage to the electronic device and/or enhance the design and development of the electronic device.

A system for dynamically load-balancing at least one redistribution element across a group of computing resources that facilitates at least an aspect of an Industrial Execution Process in an M:N working configuration is illustrated. The system is configured to: access from a central or distributed data store, a configuration component operational data and capabilities or characteristics associated with the M:N working configuration; identify a load-balancing opportunity to trigger redistribution of a redistribution element to a redistribution target selected from a redistribution target pool defined by remaining computing resource components associated with the M:N computing resource working configuration; select at least one redistribution target for redeployment; redeploy the at least one redistribution element to the redistribution target; determine redeployment to the at least one selected redistribution target to be a viable redeployment; and execute the Industrial Execution Process utilizing the at least one redistribution element at the selected redistribution target.