Abstract: Embodiments of present disclosure relates to method and apparatus for performing redundancy analysis of a semiconductor device. For the redundancy analysis, plurality of banks in the semiconductor device is classified to be associated with a cluster from plurality of clusters. The classification is based on one or more attributes associated with the plurality of banks. Further, at least one cluster parameter for the plurality of clusters and at least one bank parameter for the plurality of banks, is determined. One or more algorithms is mapped with the plurality of clusters, based on the at least one cluster parameter and the at least one bank parameter. The redundancy analysis of at least one bank in the plurality of clusters is performed based on the mapping.

Abstract: One example method includes receiving information concerning a threat to stored data, correlating the information with a preemptive action which, when taken, prevents harm to the stored data by the threat, implementing the preemptive action before the threat causes harm to the stored data, and taking an action to at least partly return a system associated with the stored data to a pre-threat state. The threat may be a natural disaster, or a human-caused condition, for example.

Abstract: A method of operating a system running a virtual machine that executes an application and an operating system (OS) includes performing first address translation from first virtual addresses to first physical addresses, identifying faulty physical addresses among the first physical addresses, each faulty physical address corresponding to a corresponding first physical address associated with a faulty memory cell, analyzing a row address and a column address of each faulty physical address and specifying a fault type of the faulty physical addresses based on the analyzing of the row address and the column address of each faulty physical address, and performing second address translation from second virtual addresses to second physical addresses based on a faulty address, thereby excluding the faulty address from the second physical addresses.

Abstract: Methods and apparatus for selection of memory devices in a distributed storage network. In an embodiment, a computing device receives a data object for storage and forwards the data object to a buffer for temporary storage, the buffer comprised of a first memory devices of a first memory type. A system level storage efficiency is determined for the data object based, at least in part, on a data attribute associated with the data object. Second memory devices, of a second memory type, are selected based on the system level storage efficiency preference, and compatible dispersed storage error encoding parameters for the data object are determined. The data object is encoded using the encoding parameters to generate a plurality of encoded data slices, which are provided to the second plurality of memory devices for storage. Further, system addressing information is generated based on an identifier associated with the data object.

Abstract: Disclosed herein are methods, systems, and processes to provide and maintain data consistency during reverse replication. It is determined that a migrate operation or a reverse replication operation has been requested. Upon determining that the migrate operation or the reverse replication operation has been requested, a resynchronization identifier is set in a dirty region log (DRL) associated with a computing device that is subject to the migrate operation or the reverse replication operation. In response to the operation being the reverse replication operation, a master boot record (MBR) sector of a boot disk associated with the computing device is overwritten.

Abstract: The disclosure herein generally relates to recommender model selection, and, more particularly, to a method and system for selecting a recommender model matching user requirements. The system collects a user requirement including at least one error measure and corresponding Error Value (EV) and Error Tolerance (ET) as input. A recommendation learned model processes the user requirement and selects at least one of a plurality of recommender models as a recommender model matching the user requirement and generates a recommendation.

Abstract: Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for selecting a test suite for an API. In one embodiment, a computing entity or apparatus is configured to receive test patterns and heuristics, receive an input API, the input API comprising API specifications, parse the input API to extract the API specifications, and based at least in part on the extracted API specifications and the test patterns and heuristics, select a test suite, wherein the test suite is programmatically generated using a machine learning model and comprises one or more test routines, one or more data values, and one or more expected results.

Abstract: Aspects of the disclosure relate to application assessment. A computing platform may receive content information and manual input data corresponding to hierarchical content. The computing platform may establish a content tree indicating relationships between pages of the hierarchical content. The computing platform may receive starting/ending pages of the hierarchical content and application assessment commands. Using the content tree and in response to receipt of the application assessment commands, the computing platform may generate error information based on the starting page and the ending page by performing a holistic error analysis of the hierarchical content between the starting page and the ending page, which may include automatically populating manual input fields using the manual input data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for consensus system downtime recovery. One of the methods includes: obtaining a pre-prepare message from the primary node; multicasting a prepare message indicating an acceptance of the pre-prepare message to at least some of the primary node and the other (N?2) backup nodes; obtaining (Q?1) or more prepare messages respectively from (Q?1) or more of the backup nodes; storing the pre-prepare message and the (Q?1) or more prepare messages; multicasting a commit message to at least some of the primary node and the other backup nodes, the commit message indicating that the one backup node agrees to the (Q?1) or more prepare messages; and obtaining, respectively from Q or more nodes among the primary node and the backup nodes, Q or more commit messages each indicating that the corresponding node agrees to (Q?1) or more prepare messages.

Abstract: The present disclosure describes example service takeover methods, storage devices, and service takeover apparatuses. In one example method, when a communication fault occurs between two storage devices in a storage system, the two storage devices respectively obtain running statuses of the two storage devices. A running status can reflect current usage of one or more system resources of a particular storage device. Then, a delay duration is determined according to the running statuses, where the delay duration is a duration for which the storage device waits before sending an arbitration request to a quorum server. The two storage devices respectively send, after the delay duration, arbitration requests to the quorum server to request to take over a service. The quorum server then can select a storage device in a relatively better running status to take over a host service.

Abstract: A method and system that efficiently selects sensors without requiring advanced expertise or extensive experience even in a case of new machines and unknown failures. An abnormality detection system includes a storage unit for storing a latent variable model and a joint probability model, an acquisition unit for acquiring sensor data that is output by a sensor, a measurement unit for measuring the probability of the sensor data acquired by the acquisition unit based on the latent variable model and the joint probability model stored by the storage unit, a determination unit for determining whether the sensor data is normal or abnormal based on the probability of the sensor data measured by the measurement unit, and a learning unit for learning the latent variable model and the joint probability model based on the sensor data output by the sensor.

Abstract: A computing system in a server environment receives a diagnostic data package, corresponding to a problem, from a client computing system and runs a problem-specific analyzer to generate additional problem-specific data. A root cause for the problem is identified along with a recovery action, based on the diagnostic data package and the additional problem-specific data. The root cause and recovery action are communicated to the client computing system.

Abstract: A memory device includes a mode register set configured to store a first repair mode, a second repair mode, and a second repair off mode, and a repair control circuit configured to perform a first repair operation for permanently repairing a first wordline corresponding to a defective address to a first redundancy wordline in the first repair mode, to perform a second repair operation for temporarily repairing the first wordline corresponding to the defective address to a second redundancy wordline in the second repair mode, and to turn off a repair logic that is configured to perform the second repair operation in the second repair off mode to access old data after the second repair operation.

Abstract: An industrial controller executes a control program held in non-transitory medium to: (a) open connections for the communication of data on the industrial control network, the connection subject to a timeout; (b) operate in a synchronized state with the second industrial controller to execute a same control program to communicate same control data; (c) operate in an unsynchronized state providing no-operation data to the industrial control network, the no-operation data preventing timeout of the open connections on which it is communicated.

Abstract: A computer-implemented method for determining features of a dataset that are indicative of anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) executing a time series decomposition algorithm on a portion of the features of the data to generate a first list of features, (3) implementing a plurality of traffic dispersion graphs to generate a second list of features, and (4) implementing an autoencoder and a random forest regressor to generate a third list of features.

Abstract: Methods, systems, and devices for memory data correction using multiple error control operations are described. A single command may be received to correct an error detected in data stored by a memory array. A first error control operation and a second error control operation may be implemented based on the single command. The first error control operation may be performed on the data stored by the memory array using one or more different reference voltages to read the data. The error may be determined to remain in the data after performing the first error control operation. The second error control operation may then be performed on the data stored by the memory array. The second error control operation may use one or more voltage distributions associated with the memory cells of the memory array.

Abstract: A system, computer program product, and method are presented for providing confidence values for replacement data for data that has issues indicative of errors, where the data issues, the replacement data, and confidence values are related to one or more KPIs. The method includes identifying one or more potentially erroneous data instances and determining one or more predicted replacement values for the potentially erroneous data instances. The method further includes determining a confidence value for each predicted replacement value and resolving the one or more potentially erroneous data instances with one predicted replacement value of the one or more predicted replacement values. The method also includes generating an explanatory basis for the resolution of the one or more potentially erroneous data instances.

Abstract: A data health monitoring system and method are provided which may be configured to monitor different indicators or parameters of a computing device which may affect the health of the computing device, such as which may be used in looking for early warning signs that could indicate future data loss or corruption. The system may periodically query certain data health indicators, such as S.M.A.R.T. status, disk temperature, read and write errors, etc. The system may also monitor data as it is being accessed keeping track of other indicators such as: files that are modified, when the last backed up was, protection status of the file, type of changes made to the file, which application is making changes, etc. Preferably, the combined status of each data health indicator may be rolled up into a simple message and shown to the user via notifications, reports, logs or a user interface.

Abstract: The subject technology provides for managing a data storage system. A data operation error for a data operation initiated in a first non-volatile memory die of a plurality of non-volatile memory die in the data storage system is detected. An error count for an error type of the data operation error for the first non-volatile memory die is incremented. The incremented error count satisfies a first threshold value for the error type of the data operation error is determined. The first non-volatile memory die is marked for exclusion from subsequent data operations.

Abstract: Systems and methods for are provided for predicting impending failure of a database and preemptively initiating mitigating failover actions, for example by shedding connections or redirecting connection requests to an alternate database that can fulfill resources being requested. In an example embodiment, to detect a slow or unstable database, connection wait times are monitored over a rolling window of time intervals, a quantity of intervals in which at least one excessive wait time event occurred are counted during the time window, and if the quantity exceeds a threshold, the database is deemed unavailable, thereby triggering connection adjustments.