Embodiments of the present disclosure provide a method and an apparatus for testing a depth learning chip, an electronic device, and a computer-readable storage medium. The method includes: testing a plurality of logic units in the depth learning chip. The plurality of logic units are configured to perform at least one of an inference operation and a training operation for depth learning. The method further include: obtaining one or more error units that do not pass the testing from the plurality of logic units. In addition, the method further includes: in response to a ratio of a number of the one or more error units to a total number of the plurality of logic units being lower than or equal to a predetermined ratio, determining the depth learning chip as a qualified chip.

Each redundancy group is constituted by one active program (storage control software of the active program) and N standby programs (N is an integer of two or more). Each of the N standby programs is associated with a priority to be determined as a failover (FO) destination. In the same redundancy group, FO is performed from the active program to the standby program based on the priority. For the plurality of pieces of storage control software including the active programs and the standby programs that change to be active by FO in the plurality of redundancy groups arranged in the same node, standby storage control software that can set each of the programs as a FO destination are arranged in different nodes.

A method for handling a trusted execution environment operating system crash is provided. The method includes: when it is detected, in a running process of a security service, that a TEE OS crashes, an electronic device stores a hardware status parameter of a TEE and a security context of an REE that are obtained when the TEE OS crashes, and suspends the security service; the electronic device restarts the TEE OS; the electronic device sets, based on the stored hardware status parameter of the TEE, a hardware status parameter of the TEE obtained after the TEE OS is restarted; the electronic device sets, based on the stored security context of the REE, a security context of the REE obtained after the TEE OS is restarted and a context of the TEE obtained after the TEE OS is restarted; and the electronic device restores the security service.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for failure handling. This failure handling method includes determining a sector set failure type associated with at least one failed sector set of a disk; if the sector set failure type indicates that the number of failed sector sets in the at least one failed sector set is greater than a first threshold number, generating an instruction for replacing the disk; and otherwise performing at least one of the following: migrating data from a failed sector set in which the number of failed sectors is greater than a second threshold number to a spare sector set, and performing a failure recovery for a failed sector set in which the number of failed sectors is less than or equal to the second threshold number.

Automatic application of software patches to software associated with container images based upon image relationships in a dependency tree. The computing device determines whether software associated with a base container image requires software patches. The computing device accesses dependency trees maintaining image relationships between the base container image and dependent container images. The computing device determines based upon the accessed one or more dependency trees whether the base container image has dependent container images derived from the base container image. The computing device applies software patches to the software associated with the base container image. The computing device rebuilds the base container image with the applied software patches. The computing device then rebuilds the dependent container images dependent upon the rebuilt base container image.

A programmable electronic power regulator includes a power module for controlling an actuator, a control module for actuating the power module, and an internal monitoring module for transferring the control module to an emergency operation. The internal monitoring module is configured to monitor a system state, detect a critical operating state, and output an error signal. The control module comprises: a basic controller, which is configured to output a power module control signal, and in which functions for open- and closed-loop control of the actuator are implemented, which are required for an emergency operation in a critical operating state; an additional controller, in which functions that are not needed for emergency operation are implemented; and a controller disconnection point, which connects the basic controller with the additional controller via a control connection, and which is configured to at least partially disconnect the control connection upon receipt of the error signal.

Apparatus and methods an artificial intelligence method of reducing failure in an informational flow of a data stream controlled by an Extract Transform Load process using a machine learning (“ML”) model training system are provided. The method may include deploying a software sensor that periodically captures data points for an extract job executed during an extract phase of the process. The method may also include building a behavior profile concurrently with the receipt of each of the data points. The method may further include comparing the behavior profile to behavior profiles stored in an Adverse Behavior Model database and behavior profiles stored in a Normal Behavior Model database. When the behavior profile is determined to have a threshold number of match points matching the behavior profile to behavior profiles in the Adverse Behavior Model database, the method may include increasing a target database storage capacity.

A method includes writing sets of encoded data slices to storage units of a storage network in accordance with error encoding parameters, where for a set of encoded data slices, the error encoding parameters include an error coding number and a decode threshold number, the error coding number indicates a number of encoded data slices that results when a data segment is encoded using an error encoding function and the decode threshold number indicates a minimum number needed to recover the data segment. The method further includes monitoring processing of the writing the sets of encoded data slices to produce write processing performance information. When the write processing performance information compares unfavorably to a desired write performance range, the method further includes adjusting at least one of the error coding number and the decode threshold number to produce adjusted error encoding parameters for writing subsequent encoded data slices.

A method is provided for a hyper-converged storage-compute system to implement an active-active failover architecture for providing Internet Small Computer System Interface (iSCSI) target service. The method intelligently selects multiple hosts to become storage nodes that process iSCSI input/output (I/O) for a target. The method further enables iSCSI persistent reservation (PR) to handle iSCSI I/Os from multiple initiators.

A plurality of computing devices are communicatively coupled to each other via a network, and each of the plurality of computing devices is operably coupled to one or more of a plurality of storage devices. One or more of the computing devices and/or the storage devices may be used to rebuild data that may be lost due to a power failure.