Camera heads and associated systems, methods, and devices for inspecting and/or mapping pipes or cavities are disclosed. A camera head may be coupled to a push-cable and may include one or more image sensors to capture images and/or videos from interior of the pipe or cavity. One or more multi-axis sensors may be disposed in the camera head to sense data corresponding to movement of the camera head within the pipe or cavity. The images and/or videos captured by the image sensors may be used in conjunction with the data sensed by the multi-axis sensors to generate information pertaining to the pipe or cavity may be generated.

A method, apparatus, and system for handling a failure of a hardware cryptography/compression accelerator is disclosed. The operations comprise: detecting that a hardware cryptography/compression accelerator at a first data storage system has failed; determining one or more failed cryptography and/or compression operation tasks that were submitted to the hardware cryptography/compression accelerator but were not completed due to the failure of the hardware cryptography/compression accelerator; and performing a remedial operation in response to the hardware cryptography/compression accelerator failure to prevent a systemic failure.

Systems, computer program products, and methods are described herein for evaluating, validating, correcting, and loading data feeds based on artificial intelligence input. The present invention may be configured to receive a data feed from a source for loading to a target data structure, analyze, based on historical feed data, metadata of the data feed to determine a likelihood of the data feed failing to load, and determine whether the likelihood of the data feed failing to load satisfies a threshold. The present invention may be configured to load the data feed to the target data structure, determine, after loading the data feed to the target data structure, whether the data feed failed to load, and either correct errors in the data feed or add error-containing portions of the data feed to a failed data log.

Operations include diagnosing performance hotspots with minimal performance impact. A system selectively extracts a subset of in-memory application data, for failure analysis, based on application metadata associated with an application. The selective data extraction may be used to generate succinct reports that are customized to explore the specific vulnerabilities of each particular application. Application metadata identifies application data attributes for value extraction. The application metadata may identify, for example, a client attribute which indicates the client which requested the execution of a failed operation (or execution of an operation with a failed sub-function). The application metadata may identify a particular function, associated with an operation, that is to be analyzed in case of operation failure. The application metadata may identify a thread-local variable of a thread, executing an operation, that is to be analyzed in case of operation failure.

The present disclosure relates to computer-implemented methods, software, and systems for an automatic recovery job execution through a scheduling framework in a cloud environment. One or more recovery jobs are scheduled to be performed periodically for one or more registered service components included in a service instance running on a cluster node of a cloud platform. Each recovery job is associated with a corresponding service component of the service instance. A health check operation is invoked at a service component based on executing a recovery job at the scheduling framework corresponding to the service component. In response to determining that the service component needs a recovery measure based on a result from the health check operation, a recovery operation is invoked as part of executing a set of scheduled routines of the recovery job. Implemented logic for the recovery operation is stored and executed at the service component.

A computer-implemented method according to one embodiment includes simulating, for a predetermined time period, output of a first task that periodically runs within a system to create a simulated output, comparing the simulated output to actual output of the first task for the predetermined time period, and generating an alert in response to determining that the simulated output does not match the actual output for the predetermined time period.

An electronic computing system preserves a pre-error state of a processing unit by receiving a first stream of inputs; buffering the first stream of inputs to generate a buffered stream of inputs identical to the first stream of inputs; conveying the first stream to a primary instance of a first program; conveying the buffered stream to a secondary instance of the first program; executing the primary instance on the first stream in real time; executing the secondary instance on the buffered stream with a predefined time delay with respect to execution of the primary instance on the first stream; detecting an error state resulting from execution of the primary instance; and in response to detecting the error state, pausing the secondary instance and preserving a current state of the secondary instance, wherein the current state of the secondary instance corresponds to a pre-error state of the primary instance.

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.

A computing device operates an application to receive and implement new functionality from a network service. If the new functionality causes the application to fail, the application can automatically discard or ignore the new functionality when the application is re-launched.

An apparatus comprising: a model to generate adjusted tuning parameters of a thread scheduling policy based on a tradeoff indication value of a target system; and a workload monitor to: execute a workload based on the thread scheduling policy; obtain a performance score and a power score from the target system based on execution of the workload, the performance score and the power score corresponding to a tradeoff indication value; compare the tradeoff indication value to a criterion; and based on the comparison, initiate the model to re-adjust the adjusted tuning parameters.