Abstract: A semiconductor device includes a master circuit which outputs a first write request signal for requesting to write data, a bus which receives the data and the first write request signal, a bus control unit which is arranged on the bus, generates an error detection code for the data and generates a second write request signal which includes second address information corresponding to first address information included in the first write request signal and memory controllers which each write the data into a storage area of an address designated by the first write request signal and writes the error detection code into a storage area of an address designated by the second write request signal in the storage areas of memories.

Abstract: Techniques provide for data rebuilding in a storage system. The techniques involve: in response to failure of a first disk in the storage system, determining a second disk having a high risk of failure in the storage system; determining whether the second disk contains a second data block that is associated with a first data block to be rebuilt in the first disk, the first and second data blocks being from a same data stripe in the storage system; and in response to determining that the second disk contains the second data block and the second data block has not yet been replicated into a third disk for backup in the storage system, reading the second data block from the second disk to rebuild the first data block, and replicating the read second data block into the third disk.

Abstract: A computer-implemented method according to one embodiment includes receiving at a storage drive, from a host, a data request, implementing the data request at the storage drive, utilizing a streaming mode, and returning, by the storage drive, a confirmation to the host, in response to implementing the data request at the storage drive utilizing the streaming mode.

Abstract: An API monitoring and validation tool that may be provided as part of a web-based application testing and deployment environment. The environment may enable developers to develop web-based applications, validate and test APIs used by the applications and deploy the applications. The API monitoring and validation tool may perform validation of a service endpoint used by an application. As part of the validation, a user may provide information regarding the form and content of a successful response to a call to the service endpoint is. The monitoring may measure performance metrics and obtain information regarding the causes of errors and failures. The monitoring can identify errors and failures that occur during API call chains to service endpoints.

Abstract: The disclosure facilitates rerouting data traffic of applications. A failover request is received by a failover application including an application identifier of a main application, the failover application indicating at least one sub-application and a target data source. The failover application selects a configuration data set of the main application based on the application identifier, wherein the selected configuration data set defines an address mask of the target data source associated with the at least one sub-application. The failover application generates failover instructions for activating data traffic routing of the at least one sub-application to the target data source based on the address mask of the target data source. The failover application provides the generated failover instructions to a data traffic manager associated with the main application, whereby data traffic of the at least one sub-application is routed to the target data source by the data traffic manager.

Abstract: Systems, methods, and software can be used to identify functions prone to logic errors in software components using binary static analysis. In some aspects, one computer-implemented method includes identifying a function defined within a binary software component; determining one or more complexity characteristics of the function based on included instructions; determining, based on the complexity characteristics, whether the function is likely to produce errors when the instructions included in the function are executed by a processor; and in response to determining that the function is likely to contain errors, generating an indication that the function requires further review.

Abstract: Generation of scripts that facilitate execution of tasks within different run-time environments that utilize different languages and syntax is provided herein. A system comprises a memory that stores executable components and a processor, operatively coupled to the memory, that executes the executable components. The executable components comprise an identification manager component that identifies one or more elements of input data. The input data can comprise test data that comprises respective identifications of an input variable, an output variable, a test vector comprising an initial value of a test input, and an expected output variable against which test results are compared. The executable components can also comprise a template tool component that generates output data that facilitates execution of tasks within different run-time environments that utilize different languages and syntax.

Abstract: An information handling system includes a storage device and a processor. The storage device stores Power-On Self Test (POST) code, diagnostic code, and remediation code. The POST code determines whether an error occurred while booting the information handling system. If an error occurred, the diagnostic code determines whether the error is associated with a configuration setting of the information handling system being in a first state. If the error is associated with the configuration setting being in the first state, the remediation code changes the first configuration setting from the first state to a second state, and reboots the information handling system.

Abstract: Logging includes: obtaining an event designating message (EDM) comprising: a set of keys, or a type of designation corresponding to the set of keys, or both; looking up a set of values associated with the set of keys in a key-value store storing a plurality of keys and their corresponding values; constructing an event using the set of keys and values corresponding to the set of keys; and outputting the constructed event.

Abstract: A test platform provides modular test automation. The modular test automation may include defining modular segments of a test configuration, and testing an Application Under Test (“AUT”) based on the modular segments. The test configuration may define a specific flow or execution ordering for the modular segments. The flow may be changed by reordering the segments, modularly adding new segments anywhere in the flow, removing segments, modifying individual segments without affecting other segments, defining a particular segment once and reusing the particular segment in two or more different test configurations, and/or carrying over a change made to the particular segment in a first test configuration to the particular segment of a second test configuration automatically. The modular test automation may also include modularly selecting a set of nodes for testing the AUT according to the modular segments of the flow.

Abstract: A display device may include an image processor which is configured to process an image signal and includes a detector configured to determine a group of parameters by detecting a waveform of a detected partial discharge and measuring each of parameters of the waveform in the form of a binary number and a controller configured to identify the partial discharge by comparing the determined group with previously stored groups; and a display unit configured to display the image signal.

Abstract: An anomaly detection and resolution system (ADRS) is disclosed for automatically detecting and resolving anomalies in computing environments. The ADRS may be implemented using an anomaly classification system defining different types of anomalies (e.g., a defined anomaly and an undefined anomaly). A defined anomaly may be based on bounds (fixed or seasonal) on any metric to be monitored. An anomaly detection and resolution component (ADRC) may be implemented in each component defining a service in a computing system. An ADRC may be configured to detect and attempt to resolve an anomaly locally. If the anomaly event for an anomaly can be resolved in the component, the ADRC may communicate the anomaly event to an ADRC of a parent component, if one exists. Each ADRC in a component may be configured to locally handle specific types of anomalies to reduce communication time and resource usage for resolving anomalies.

Abstract: Systems, apparatuses and methods may provide for technology to conduct, by a storage device, a state analysis of the storage device based on an assert log associated with a failure condition in the storage device. The technology may also return, by the storage device, the storage device to service if the state analysis indicates that the storage device is operable. Additionally, the technology may remove, by the storage device, the storage device from service if the state analysis indicates that the storage device is inoperable.

Abstract: A fault detection circuit may be used to determine if voltage supplied by a voltage regulator as power to sequential logic circuitry falls below a minimum voltage expected to be required by the sequential logic circuitry for proper operation. Information regarding voltage levels supplied to the sequential logic circuitry prior to such an occurrence may be written to a memory, for example to allow for further analysis.

Abstract: A solution is proposed for managing a plurality of computing machines. A corresponding method comprises causing each computing machine of at least part of the computing machines to execute a management activity on the computing machine; receiving a corresponding result of the execution of the management activity on each computing machine; determining a success fingerprint according to one or more characteristics of each of at least part of one or more wherein the corresponding result is indicative of a success of the execution of the management activity thereon; calculating a similarity index for each of one or more failure ones of the computing machines wherein the corresponding result is indicative of a failure of the execution of the management activity thereon; and prioritizing the computing machines which failed to accept a policy according to the corresponding similarity indexes.

Abstract: One example method includes identifying, in a computing system, an aggregate data set that includes both data used by a first user and data used by a second user, examining file metadata associated with the data in the aggregate data set and, based on the examination of the file metadata, determining whether or not any data in the aggregate data set is shared by the first and second users. When a determination is made that some data in the data set is shared by the first and second users, the method includes further determining how much of the data is shared, assigning a strength of connection between the first user and the second user based on the extent to which the first user and second user share data and, based on the strength of connection, taking an action to improve operation and/or configuration of the computing system.

Abstract: A disclosed method is performed at a fault-tolerant object-based storage system including M data storage entities, each is configured to store data on an object-basis. The method includes obtaining a request to store N copies of a data object and in response, storing the N copies of the data object across the M data storage entities, where the N copies are distributed across the M data storage entities. The method additionally includes generating a first parity object for a first subset of M copies of the N copies of the data object, where the first parity object is stored on a first parity storage entity separate from the M data storage entities. The method also includes generating a manifest linking the first parity object with one or more other subsets of M copies of the N copies of the data object.

Abstract: The invention relates to a server configuration drift monitoring tool that allows for active monitoring of server configurations. The Monitoring Tool has little overhead and may be quickly and easily integrated with a current or new environment. With simple configuration files available, an administrator may customize necessary settings. The Monitoring Tool may run on various platforms, including Platform as a Service (PaaS) offerings, and may be compiled into a program component. The Monitoring Tool allows users and administrators to access a dashboard (via any web browser or mobile device), showing current connections and the ability to select components/devices to do a side by side comparison.

Abstract: Capability information for a sensor agent is received. The capability information includes one or more metrics that the sensor agent is configured to provide. The sensor agent is configured in accordance with an initial configuration including a first level of granularity for test data. When the test data is received from the sensor agent, a value of at least one metric from the one or more metrics is tested in a machine learning module. The value is classified based on an output from the machine learning module. The initial configuration of the sensor agent is automatically adjusted to adaptively define a second level of granularity for the test data provided by the sensor agent.

Abstract: A preprocessor into which time-series sensor data is input includes a correspondence table associating sensor data with a physical unit. A physical extraction unit that obtains sensor data, and specifies a physical unit based on the correspondence table. A latent variable generation unit specifies a nonlinear function from the physical unit, and generates a latent variable for applying the sensor data to the specified nonlinear function. A latent variable addition unit obtains time-series sensor data and sets the data as a variable, applies the obtained time-series sensor data to the latent variable to generate time-series data and sets the data as a variable, and adds the generated time-series data to the obtained time-series sensor data to generate a data set of a plurality of variables. Then a relational expression estimation-processing unit that analyzes a relationship between the variables of the data set, and generates a linear relational expression.