A system and method for backing up workloads for multiple tenants of a cloud computing system are disclosed. A method of backing up workloads for multiple tenants of a computing system includes triggering an archival process according to an archival policy set by a tenant, and executing the archival process by reading backup data of the tenant stored in a backup storage device of the computer system and transmitting the backup data to an archival store designated in the archival policy, and then deleting or invalidating the backup data stored in the backup storage device.

A memory device comprises a memory control unit including a processor configured to control operation of the memory array according to a first memory management protocol for memory access operations, the first memory management protocol including boundary conditions for multiple operating conditions comprising program/erase (P/E) cycles, error management operations, drive writes per day (DWPD), and power consumption; monitor operating conditions of the memory array for the P/E cycles, error management operations, DWPD, and power consumption; determine when a boundary condition for one of the multiple operating conditions is met; and in response to determining that a first boundary condition for a first monitored operating condition is met, change one or more operating conditions of the first memory management protocol to establish a second memory management protocol for the memory access operations, the second memory management protocol including a change boundary condition of a second monitored operating condition.

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.

Methods, systems, and devices for maintenance command interfaces for a memory system are described. A host system and a memory system may be configured according to a shared protocol that supports enhanced management of maintenance operations between the host system and memory system, such as maintenance operations to resolve error conditions at a physical address of a memory system. In some examples, a memory system may initiate maintenance operations based on detections performed at the memory system, and the memory system may provide a maintenance indication for the host system. In some examples, a host system may initiate maintenance operations based on detections performed at the host system. In various examples, the described maintenance signaling may include capability signaling between the host system and memory system, status indications between the host system and memory system, and other maintenance management techniques.

A request manager analyzes API calls from a client to a host application for state and performance information. If current utilization of host application processing or memory footprint resources exceed predetermined levels, then the incoming API call is not forwarded to the application. If current utilization of the host application processing and memory resources do not exceed the predetermined levels, then the request manager quantifies the processing or memory resources required to report the requested information and determines whether projected utilization of the host application processing or memory resources inclusive of the resources required to report the requested information exceed predetermined levels. If the predetermined levels are not exceeded, then the request manager forwards the API call to the application for processing.

Provided is an anomaly pattern detection system including an anomaly detection device connected to one or more servers. The anomaly detection device may include an anomaly detector configured to model input data by considering all of the input data as normal patterns, and detect an anomaly pattern from the input data based on the modeling result.

Techniques manage a redundant array of independent disks. In such a technique, a response time of a first storage device in the RAID is compared to a first threshold. In response to the response time of the first storage device exceeding the first threshold, the first storage device is configured as a pseudo-degraded storage device, such that the pseudo-degraded storage device is responsive to write requests only.

An electronic device including an application processor and a communication processor. The communication processor including a resource memory, the communication processor configured to monitor an occupancy rate of the resource memory, determine whether the electronic device is in an idle state, forcibly release a network connection, clear the resource memory, and reconnect the network connection.

A method and/or system for automated detection and automated remediation of anomalies in Robotic Process Automation (RPA) environment is disclosed. The method comprises auto discovering resources (RPA components and its dependencies) in an RPA platform. The discovered resources are monitored though observation metrics whose values are obtained by executing pre-defined scripts. The obtained values are validated against threshold values to determine if there are any anomalies, wherein the threshold values may either be static values or dynamic values. If there is a breach of threshold, a remediation plan is automatically executed causing the remediation of anomalies. The system is trained to determine the dynamic threshold values through machine learning models which are developed and trained through metrics data and by determining error patterns from the historic unstructured log data.

A system of monitoring and optimizing computing resources usage for computing application may include predicting a first performance metric for job load capacity of a computing application for optimal job concurrency and optimal resource utilization. The system may include generating an alerting threshold based on the first performance metric. The system may further include, in response to a difference between the alerting threshold and a job load of the computing application within an interval exceeding a threshold, predicting a second performance metric for job load capacity of the computing application for optimal job concurrency and optimal resource utilization. The system may further include, in response to a difference between the first performance metric and the second performance metric exceeding a difference threshold, updating the alerting threshold with a job load capacity with the optimal resource utilization rate corresponding to the second performance metric.