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.

Exemplary methods, apparatuses, and systems include receiving a plurality of read operations. The read operations are divided into a current set of a sequence of read operations and one or more other sets. The size of the current set is a first number of read operations. An aggressor read operation is selected from the current set. A first data integrity scan is performed on a victim of the aggressor and a first indicator of data integrity is determined based on the first data integrity scan. A scaling factor is determined using the indicator of data integrity and a number of program erase cycles for the portion of memory. The set size of read operations is adjusted to a second number of read operations using the scaling factor for a subsequent set.

A method and apparatus are described. The method includes receiving a set of data streams including data values generated by a sensor associated with the operation of a component in a system at points in time and generating an anomaly data value for the received data values. The method further includes applying a machine learning algorithm to the received data values and a subset of data values previously received to generate expected data values at points in time beyond the current point in time, generating an expected anomaly data value for each of the expected data values, and identifying an operational anomaly for the component at a point in time beyond the current time based on the expected anomaly data value. The apparatus includes an input interface for receiving the data streams and a processor for processing the received data values to identify an operational anomaly as described above.

During operation, an electronic device may store, in memory, information associated with operation of the electronic device, such as during communication and processing of one or more packets or frames. Furthermore, an error-event monitor in the electronic device may, during a time interval, analyze at least a portion of the stored information to detect an occurrence of an error event in one or more types of error events in the electronic device. When the error event occurs during the time interval, the electronic device may perform a remedial action and may persist, in the memory, at least a second portion of the stored information associated with the error event. Otherwise, when the error event does not occur during the time interval, the electronic device may overwrite, in the memory, the stored information with additional information associated with operation of the electronic device during subsequent communicating and processing.

An apparatus to facilitate scalable runtime validation for on-device design rule checks is disclosed. The apparatus includes a memory to store a contention set, one or more multiplexors, and a validator communicably coupled to the memory. In one implementation, the validator is to: receive design rule information for the one or more multiplexers, the design rule information referencing the contention set; analyze, using the design rule information, a user bitstream against the contention set at a programming time of the apparatus, the user bitstream for programming the one or more multiplexors; and provide an error indication responsive to identifying a match between the user bitstream and the contention set.

A method for mitigating errors in the transportation of configuration data may include identifying, at a development system, dependent configuration data associated with a first transport request. The dependent configuration data may implement a customization to a software application hosted at a production system. A reference table identifying the dependent configuration data may be sent to the production system. A missing object list identifying dependent configuration data absent from the production system may be generated at the production system based on the reference table. The missing object list may be sent to the development system where a corrective action may be performed such that the dependent configuration data identified by the missing object list as being absent from the production system is sent to the production system in the first transport request and/or a second transport request. Related systems and articles of manufacture, including computer program products, are also provided.

Embodiments provide enhanced performance diagnosis in a network computing environment. In response to an occurrence of a performance issue for a node while under operating conditions, common logs for applications on the node are analyzed. The applications are respectively registered in advance for diagnosis services. The applications each register rules in advance for the diagnosis services. At a time of the performance issue, debug programs are automatically issued to generate debug level logs respectively for the applications. Debug level logs are analyzed according to the rules to determine a root cause of the performance issue. A potential solution to the root cause of the performance issue is determined using the rules, without having to recreate the operating conditions occurring during the performance issue. The potential solution to rectify the root cause of the performance issue is executed without having to recreate the operating conditions occurring during the performance issue.

This disclosure describes automatically collecting, analyzing, and remediating operational issues with respect to systems executing within a network. For example, a service provider network may include a monitoring service may generate notifications related to operational issues upon detection of operational issues within a system executing within the service provider network. The monitoring service may provide one or more notifications related to an aggregation service that may aggregate the one or more notifications into a standardized format. Contextual information related to the operational issues may be automatically gathered by an analytics service, which may analyze the contextual information to determine a potential cause of the operational issues. Based on the potential cause, a remediation service may automatically remediate the operational issues.

In one embodiment, a system for managing communication connections in a virtualization environment includes a plurality of host machines implementing a virtualization environment, wherein each of the host machines includes a hypervisor, at least one user virtual machine (user VM), and a distributed file server that includes file server virtual machines (FSVMs) and associated local storage devices. Each FSVM and associated local storage device are local to a corresponding one of the host machines, and the FSVMs conduct I/O transactions with their associated local storage devices based on I/O requests received from the user VMs. Each of the user VMs on each host machine sends each of its respective I/O requests to an FSVM that is selected by one or more of the FSVMs for each I/O request based on a lookup table that maps a storage item referenced by the I/O request to the selected one of the FSVMs.

A storage controller has an operating system (OS) and power control firmware configured to manage use of battery power during a power outage event. The OS specifies to the power control firmware first and second sets of physical components that should be shed by power control firmware during a two-phase vault process. Upon a power failure, the power control firmware turns off power to the first set of physical components and notifies the OS of the power failure. The OS determines whether to abort or continue the vault process. If the OS aborts the vault process, the power control firmware restores power to the first set of physical components. If the OS continues the vault process, the power control firmware turns off power to the second set of physical components, the OS saves application state, and moves all data from volatile memory to persistent memory.