In one embodiment, a system for managing a virtualization environment includes a set of host machines, each of which includes a hypervisor, virtual machines, and a virtual machine controller, and a data migration system configured to identify one or more existing storage items stored at one or more existing File Server Virtual Machines (FSVMs) of an existing virtualized file server (VFS). For each of the existing storage items, the data migration system is configured to identify a new FSVMs of a new VFS based on the existing FSVM, send a representation of the storage item from the existing FSVM to the new FSVM, such that representations of storage items are sent between different pairs of FSVMs in parallel, and store a new storage item at the new FSVM, such that the new storage item is based on the representation of the existing storage item received by the new FSVM.

A data storage device restoring method is provided, which is adapted to a data storage device. The data storage device includes an SSD controller, a power management circuit, a non-volatile memory, and a reset circuit. The data storage device restoring method includes: the power management circuit determines whether a normal signal from the SSD controller is received within a predetermined time; if not, the power management circuit resupplies power to the data storage device but stops supplying power to the non-volatile memory, thereby the SSD controller stays in a read-only memory mode to automatically execute the data storage device restoring process.

To be able to support the efficient handling of problems during maintenance. A management board determines an accomplishment sequence of handling manipulation for failures on a maintenance target. The management board includes a CPU, memory, and a storage device. The memory or the storage device stores: condition information associating the handling manipulation with a condition concerning at least accomplishment or non-accomplishment of the manipulation; and failure handling information that associates a failure occurring on the maintenance target with the handling manipulation. The CPU is configured to determine the accomplishment sequence of the handling manipulations so that multiple handling manipulations for multiple failures occurring on the maintenance target satisfy the condition.

A vehicular control apparatus is used in an onboard system provided with a plurality of information processors mutually connected via a communication bus, and includes a storage section for storing information, and an arithmetic section for executing a process based on the information stored in the storage section. The information contains first management information relating to a security abnormality as a communication data abnormality owing to security attack from outside the onboard system, and second management information relating to a safety abnormality as a communication data abnormality owing to an abnormality in the onboard system. The first management information contains first limit condition information indicating a first limit condition for executing a security coping with the security abnormality. The second management information contains second limit condition information indicating a second limit condition for executing a safety coping with the safety abnormality. Upon detection of the communication data abnormality in the onboard system, the arithmetic section determines a coping content to the detected communication data abnormality based on the first management information and the second management information.

To improve the reliability of nodes that are utilized by a cloud computing provider, information about the entire lifecycle of nodes can be collected and used to predict when nodes are likely to experience failures based at least in part on early lifecycle errors. In one aspect, a plurality of failure issues experienced by a plurality of production nodes in a cloud computing system during a pre-production phase can be identified. A subset of the plurality of failure issues can be selected based at least in part on correlation with service outages for the plurality of production nodes during a production phase. A comparison can be performed between the subset of the plurality of failure issues and a set of failure issues experienced by a pre-production node during the pre-production phase. A risk score for the pre-production node can be calculated based at least in part on the comparison.

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.

This application relates to a computing system and method for an automated program error repair. In one aspect, the computing system includes a storage, a preprocessing processor, and an automated error repair processor. The storage stores a program code. The preprocessing processor acquires the program code from the storage and preprocesses the program code. Preprocessing includes tokenizing the program code with tokens, converting the tokens into vectors, and adding location information for the tokens. The automated error repair processor receives the preprocessed program code as an input from the preprocessing processor, detects an error in the preprocessed program code, corrects the detected error, and outputs the error-corrected program code. Detecting and correcting the error are performed based on a deep learning result and the location information for the tokens.

An analytics server receives from client computing devices end-user events. Each client computing device is operated by an end-user to access an application at a web server based on the end-user events resulting in calls being passed through a proxy to the web server. The analytics server receives from the proxy the calls being made to the web server, and receives return responses from the web server being passed through the proxy. The return responses correspond to activities being performed within the application. The end-user events are correlated with the corresponding calls and return responses from the proxy. Respective correlated end-user events, calls and return responses are translated into respective event vectors. The respective event vectors are processed to determine similarities among the client computing devices. The similar activities are associated with a quality indicator to identify anomalies within the application for corrective action to be taken.

A BER corresponding to a group of memory cells programmed via a programing signal having one or more program step characteristics is determined. The determined BER and a target BER is compared. In response to the determined BER being different than the target BER, one or more program step characteristics are adjusted to adjust the determined BER to the target BER.

An example computing device includes a power supply. The computing device also includes a storage device to receive power from the power supply during an error collection operation. The storage device is also to receive power via radio waves from a client device during a diagnostic operation. The computing device further includes a controller to receive error information associated with a component of the computing device. The controller is also to store the error information in the storage device during the error collection operation.