Abstract: A system for generating a robustness score for hardware components, nodes, and clusters of nodes in a computing infrastructure is provided. The system includes a memory and at least one processing device coupled to the memory. The processing device is to obtain first telemetry data associated with a selected portion of a computing infrastructure, and the selected portion includes a first node and a first hardware component. The processing device is further to obtain first metadata associated with the selected portion, input one or more telemetry inputs corresponding to the first telemetry data into a machine learning model, input one or more metadata inputs corresponding to the first metadata into the machine learning model, and generate, from the machine learning model, a first robustness score for the first hardware component representing a health state of the first hardware component.

Abstract: An error documentation system including tools to collect and analyze application error data for individual development teams and tools to share documented defects and solutions across development teams during any stage of development cycle. The system may receive and analyze event logs for error events triggered by applications on end-user devices. The system may automatically generate defect tickets and/or ticket entries for defects identified in event logs. The system may train one or more machine learning (ML) models to correlate input with identified defects from a defects database. In response to identifying correlated identified defects, the system may generate ticket entries indicating the correlated identified defects and associated solutions for the defects. The system may provide an interface for users to query the data stored in the database.

Abstract: A memory system may include a nonvolatile memory device comprising a first area and a second area having a higher data I/O operation speed than the first area, and a controller suitable for performing a first read operation on hot data having a hot property, among data stored in the first area. The controller may control the nonvolatile memory device to copy the hot data into the second area during the first read operation, and access the hot data copied in the second area, when a second read operation on the hot data is requested after the first read operation.

Abstract: Described systems and techniques enable prediction of a state of an application at a future time, with high levels of accuracy and specificity. Accordingly, operators may be provided with sufficient warning to avert poor user experiences. Unsupervised machine learning techniques may be used to characterize current states of applications and underlying components in a standardized manner. The resulting data effectively provides labelled training data that may then be used by supervised machine learning algorithms to build state prediction models. Resulting state prediction models may then be deployed and used to predict an application state of an application at a specified future time.

Abstract: Aspects of the present invention disclose a method and system for anomaly detection for a data source. The method includes one or more processors identifying unexpected values of monitoring measurands in a monitored time series utilizing an anomaly detection algorithm. A data source provides sensor data, including values of a first group of measurands, which include monitoring measurands. The method further includes determining that values of a second group of one or more of the measurands of a subset of sensor data indicates an anomaly utilizing the anomaly detection algorithm. The method further includes sending anomalous data indicative of the subset of sensor data to a root cause analysis system and receiving corresponding feedback that is indicative of a result of a root cause analysis of the subset of sensor data and comprises a third group of the measurands. The method further includes adapting the anomaly detection algorithm.

Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving a request to monitor for anomalies from one or more data sources; analyzing time-series data from the one or more data sources; generating a recommendation for handling the determined anomaly, the recommendation generated by performing one or more of a root cause analysis, a heuristic analysis, and an incident similarity analysis; and reporting the anomaly and recommendation.

Abstract: Disclosed herein are embodiments of systems and methods for query-attempt processing in a database environment. In an embodiment, a database platform receives a query, and operates a first query manager of the database platform to manage a first attempt to execute the query. Based on determining that the attempt to execute the query was unsuccessful, the database platform identifies a second query manager of the database platform as implementing a same version of the database platform as the first query manager, and operates the second query manager to manage a first retry attempt to execute the query.

Abstract: A method comprises receiving a notification of an issue with at least one component of a plurality of components in a computing environment. One or more machine learning algorithms are used to determine one or more components of the plurality of components impacted by the issue with the at least one component. The method further comprises collecting operational data for the at least one component and the one or more impacted components.

Abstract: Provided are a memory controller, a method of operating the memory controller, and a storage device including the memory controller. The method includes performing a first operation on a non-volatile memory; storing a first code path corresponding to pieces of codes executed as the first operation is performed in a history buffer; comparing the first code path with a plurality of reference code paths related to the first operation; and identifying whether the first operation is abnormally performed based on a result of the comparison.

Abstract: Managing data center recovery from failure events can include a failure event platform having aspects provided via a user interface that integrates multiple failure and recovery management and execution features. The features can include, among others, application drift monitoring between production and recovery environments, real-time health checks of system components, user-modifiable scripting for prioritizing and customizing data center recovery actions, and a recovery execution tool.

Abstract: A cache control apparatus includes a data unit configured to store data on an index-specific basis, a tag unit configured to store, on the index-specific basis, a tag and a flag indicating whether the data has an uncorrectable error, and a control unit configured to refer to the flag, upon detecting a tag hit by performing a read access to the tag unit, to determine whether an uncorrectable error exists in the data corresponding to the tag hit, wherein the control unit performs process scheduling such that the read access to the tag unit and another access to the tag unit are performed simultaneously.

Abstract: 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.

Abstract: A computer-implemented method for executing a software application in a virtual machine executing on a data processing device includes receiving software code for a software application, determining application programming interfaces referenced by the software code, determining portions of an operating system accessed by the software code and creating an application container in the virtual machine. The method also includes application programming interfaces referenced by the software code inside the application container, portions of the operating system accessed by the software code inside the application container and executing the software application inside the application container on the virtual machine.

Abstract: A method for processing data between host computer and CPLD provides a host computer, a circuit board comprising a UART unit, a pre-debugged hardware, and a CPLD. The UART unit communicates with the host computer via UART. The method further provides the CPLD coupled between the UART unit and the pre-debugged hardware and allows the CPLD to receive data from the host computer via the UART unit and to analyze the data. According to the method, the CPLD debugs the pre-debugged hardware according to the analyzed data and obtains a result of debugging. The CPLD outputs the result and allows the CPLD to transmit the result to the host computer via the UART unit. A system using the method is also provided.

Abstract: A technique is disclosed for generating rebuild data of a RAID configuration having one or more failed drives. The RAID configuration includes multiple sets of drives coupled to respective computing nodes, and the computing nodes are coupled together via a network. A lead node directs rebuild activities, communicating with the other node or nodes and directing such node(s) to compute partial rebuild results. The partial rebuild results are based on data of the drives of the RAID configuration coupled to the other node(s). The lead node receives the partial rebuild results over the network and computes complete rebuild data based at least in part on the partial rebuild results.

Abstract: An illustrative method includes detecting, by a cloud based storage system services provider based on a problem signature, that a storage system has experienced a problem that is associated with the problem signature; and deploying, without user intervention, one or more corrective measures that modify the storage system to resolve the problem.

Abstract: A method performed by a computing device, of configuring a new design of a new data storage system (DSS) having initial configuration parameters is provided. The new design includes an initial plurality of storage drives. The method includes (a) collecting operational information from a plurality of remote DSSs in operation, the operational information including numbers of writes of various write sizes received by respective remote DSSs of the plurality of remote DSSs over time; (b) modeling a number of drive writes per day (DWPD) of the initial plurality of storage drives of the new DSS based on the collected operational information from the plurality of remote DSSs and the initial configuration parameters; (c) comparing the modeled number of DWPD to a threshold value; and (d) in response to the modeled number of DWPD exceeding the threshold value, reconfiguring the new DSS with an updated design.

Abstract: In one embodiment, an exemplary method includes receiving multi-dimension information from a data domain operating system running on the server; determining that multiple drive failures occurred within a predetermined time frame based on the multi-dimension information; and extracting a list of system-level events and a timestamp of each event from the multi-dimension information. The method further includes determining a list of components impacted by the list of the system-level events based on the list of system-level events and the timestamp of each event; and determining one or more system-level events associated with one or more impacted components as root causes of the multiple drive failures based on the multi-dimension information. The method uses information from multiple regions of the DAE and correlate the information using a predetermined algorithm to automatically more efficiently identify one or more possible root causes of the multiple drive failures.

Abstract: Provided is an anomaly pattern detection system including an anomaly detection device connected to one or more servers. The anomaly detection device may include an anomaly detector configured to model input data by considering all of the input data as normal patterns, and detect an anomaly pattern from the input data based on the modeling result.

Abstract: A method for processing data exactly once using transactional stream writes includes receiving, from a client, a batch of data blocks for storage on memory hardware in communication with the data processing hardware. The batch of data blocks is associated with a corresponding sequence number and represents a number of rows of a table stored on the memory hardware. The method also includes partitioning the batch of data blocks into a plurality of sub-batches of data blocks. For each sub-batch of data blocks, the method further includes assigning the sub-batch of data blocks to a buffered stream; writing, using the assigned buffered stream, the sub-batch of data blocks to the memory hardware; updating a storage log with an intent to commit the sub-batch of data blocks using the assigned buffered stream; and committing the sub-batch of data blocks to the memory hardware.