An embodiment method of operating a CAN bus comprises coupling a first device and second devices to the CAN bus via respective CAN transceiver circuits, and configuring the respective CAN transceiver circuits to set the CAN bus to a recessive level during transmission of messages via the CAN bus by the respective first device or second devices.

A method, computer program product, and computer system are provided. An adjunct processor receives an indication to reset a message queue. The reset removes all messages from the message queue on the adjunct processor. The reset is in response to a hardware failure or an external manual operation. An operation to enqueue a message to the message queue is received. Based on completing a successful enqueue operation, the adjunct processor sets a status that includes an indication that the enqueue operation is a first operation following the reset. A requestor receives the indication of the first operation. Based on the internal count of pending messages being greater than one, the requestor requeues to the adjunct processor, the message requests, except for the first message, having outstanding replies, and resets the indication of the first enqueue operation upon the requeuing of the message requests being complete.

Methods and systems are directed to discovering problem incidents in a distributed computing system. Events corresponding to historical problems incidents for the distributed computing system are retrieved from a data base. Sets of representative events of the various historical problem incidents for the distributed computing system are determined. A runtime problem incident in the distributed computing system is characterized by runtime events. The runtime problem incident is classified as corresponding to a historical problem incident of the historical problem incidents based on the runtime events and the sets of representative events. Remedial measures used to correct the historical problem incident may be used to correct the runtime problem.

A method for allocating a plurality of virtual machines (51-55) provided on at least one host (11-15) to a virtualized network function is provided, which provides a defined functional behavior in a network and requires a total application capacity for the functional behavior, the functional behavior being provided by needed virtual machines from the plurality of virtual machines, wherein each of the at least one host has an available processing capacity which can be assigned to the virtual machines provided on the corresponding host, and each virtual machine has at least one flavor which indicates a used processing capacity of the available processing capacity of the corresponding host and which corresponds to a partial application capacity of the total application capacity provided by the corresponding virtual machine, the method comprising: —determining the total application capacity of the virtualized network function, —determining, for each of the virtual machines, the at least one flavor taking into account the available processing capacity of the host on which the corresponding virtual machine is provided, and the corresponding at least one partial application capacity, —determining the needed virtual machines from the plurality of virtual machines and needed flavors of the needed virtual machines that are required to provide the total application capacity, wherein determining the needed virtual machines and needed flavors comprises: performing an iterative process in which the needed virtual machines are dynamically determined from the plurality of virtual machines based on the total application capacity, and in which the needed flavor for each of the needed virtual machines is dynamically determined taking into account the total application capacity and the available processing capacity provided on the host on which the corresponding needed virtual machine is provided.

A processor may provide a machine learning model. The machine learning model may have an input and an output. The processor may receive input data. The input data may include log data of a queried storage medium and a queried media drive. The processor may provide the input data to the input of the machine learning model. The processor may determine, from the output of the machine learning model, a predicted failure cause category and a predicted failure probability assigned to the predicted failure cause category. The processor may provide a first prediction to a user.

Methods and apparatus are disclosed for implementing data augmentation within a storage controller of a data storage device based on machine learning data read from a non-volatile memory (NVM) array of a memory die. Some particular aspects relate to configuring the storage controller to generate augmented versions of training images for use in training a Deep Learning Accelerator of an image recognition system by rotating, translating, skewing, cropping, etc., a set of initial training images obtained from a host device and stored in the NVM array. Other aspects relate to controlling components of the memory die to generate noise-augmented images by, for example, storing and then reading training images from worn regions of the NVM array to inject noise into the images. Data augmentation based on data read from multiple memory dies is also described, such as image data spread across multiple NVM arrays or multiple memory dies.

A command is transmitted to a storage device to relocate first data that partially fills a first erase block of the storage device and second data that partially fills a second erase block of the storage device to a third erase block of the storage device, wherein the command causes the relocation of the first data and the second data while bypassing sending the data to the storage controller. An acknowledgement that the first data and the second data have been stored at the third erase block is received from the storage device.

A master database module is on a master computer node. Slave database modules are on slave computer nodes connected to the master computer node via a network. A distributed database includes executable code executed by processors on the master computer node and the slave computer nodes to receive a distributed database query at the master computer node. A query execution plan is prepared at the master computer node. The query execution plan is deployed on the slave computer nodes. The query execution plan is executed on the slave computer nodes. The slave computer nodes each perform a single node query execution analysis to selectively produce a query cancellation command. The query cancellation command is propagated to the master computer node and the slave computer nodes. The query execution plan is cancelled on the master computer node and the slave computer nodes.

A first error rate based on a first read operation performed on a memory device is obtained. An individual data unit of the memory device that satisfies a first threshold criterion associated with a defect candidate is determined. A defect verification operation on the individual data unit to obtain a second error rate is performed. The individual data unit that satisfies a second threshold criterion associated with a defect is determined.

An approach to identifying a corrective action for a data storage device (DSD), such as one implemented in a fleet of DSDs in a data center, involves receiving error data about excursions from normal operational behavior of the DSD, inputting data representing a particular excursion into a probabilistic decision network which characterizes a set of DSD operational metrics and certain DSD controller rules that represent internal controls of the DSD and corresponding conditional relationships among the operational metrics, determining from the decision network the likelihood that one or more possible causes was a contributing factor to the particular excursion, and determining a corrective action for the particular excursion based on the determined likelihood of a particular cause of the one or more possible causes. The corrective action may then be shared with the DSD for in-situ execution of corresponding self-repair operations.