Abstract: A method for facilitating root cause analysis of a software crash by core dump analysis is disclosed. The method comprises receiving a core dump file relating to a software program, identifying unique source code lines in the core dump file for each running thread at the crash time, and determining unique source code lines as conspicuous source code lines depending on an abstraction level value indicating a number of occurrences of the conspicuous source code line in different threads. Furthermore, the method comprises determining an abstraction ratio as a function of a number of conspicuous source code lines and a number of unique source code lines, evaluating whether the predefined abstraction level value has to be adjusted by determining unique source code line as a conspicuous source code line and determining an abstraction ratio, and outputting the conspicuous source code lines and an assessment value for the abstraction ratio.

Abstract: A method for managing data backup includes: making a determination that an asset source is a first type of asset source, in which a source client device comprises the asset source; generating, based on the determination, a data transfer path for a plurality of snapshots associated with the asset source between the source client device and a data protection system; initiating a backup for user data associated with the asset source to a target client device; obtaining the plurality of snapshots to the data protection system using the data transfer path, in which the user data is backed up in the target client device while at least a portion of the plurality of snapshots is obtained by the data protection system; and deleting the plurality of snapshots from the source client device, upon receipt of confirmation that the plurality of snapshots is successfully stored in the data protection system.

Abstract: A method for detecting a fault in an intelligent electronic device that includes components uses a Bayesian network. The method includes detecting a failure event in the components, obtaining a first list of cause of failures in the component using a fault tree model, computing probability of the cause of failures to obtain a second list of probable causes of failure by monitoring of information about the elements identified in the first list, identifying a root cause of failure associated with the element comprised in the component using the Bayesian network based on the second list, and initiating a function. The function may be one of restarting the element having the root cause of failure, a filtering operation for input data provided to that element; and providing an alert in the human machine interface associated with the intelligent electronic device.

Abstract: A method of generating a core dump in a User Equipment (UE) may include detecting, using at least one processor, a trigger, the trigger including an instruction to generate a core dump of the UE. The method may further include classifying, using the at least one processor, data stored in memory of the UE as Read-Write (RW) data or Read Only (RO) data. The method may further include generating, using the at least one processor, a partial core dump based on the RW data of the memory.

Abstract: A data processing system may include: a memory system including an error history region, the memory system suitable for storing in the error history region, error history data related to an internal error, and a host suitable for obtaining the error history data from the memory system by providing the memory system with an error history read command, performing failure analysis of the memory system on the basis of the obtained error history data, and controlling the memory system to clear at least a portion of the error history region by providing the memory system with an error history clear command, wherein the error history region is a memory region that is not able to be accessed with a logical address used by the host.

Abstract: A computer-implemented method, according to one embodiment, is for restoring operation of a data storage system at a disaster recovery site. The computer-implemented method includes: in response to a disaster event occurring at a primary site, receiving an inode list from a cloud storage site, and receiving configuration information from the cloud storage site. The cloud storage site includes a backup copy of data that is stored at the primary site. Moreover, the inode list and the configuration information are used to construct a filesystem at the disaster recovery site. The filesystem at the disaster recovery site does not include a copy of the data that is stored at the primary site, but rather the filesystem includes a plurality of metadata stubs. The filesystem is further used to satisfy I/O commands that are received.

Abstract: Failover methods and systems for a networked storage environment are provided. In one aspect, a read request associated with a first storage object is received, during a replay of entries of a log stored in a non-volatile memory of a second storage node for a failover operation initiated in response to a failure at a first storage node. The second storage node operates as a partner node of the first storage node. The read request is processed using a filtering data structure that is generated from the log prior to the replay and identifies each log entry. The read request is processed when the log does not have an entry associated with the read request, and when the filtering data structure includes an entry associated with the read request, the requested data is located at the non-volatile memory.

Abstract: An information technology (IT) operations platform is described that enables users to execute one or more executable actions from a set of executable actions presented in a prioritized order based on historical data. In response to identifying an occurrence of a type of incident in an IT environment, the IT operations platform generates a workbook based on a customizable workbook template. The customizable workbook template includes a plurality of tasks grouped into a plurality of phases for responding to occurrences of the type of incident, and each task of the plurality of tasks is associated with a respective set of suggested executable actions for completing the corresponding task. The IT operations platform then causes the display of a graphical user interface (GUI) including a representation of the workbook, including interface elements representing the respective set of suggested executable actions displayed in the prioritized order.

Abstract: Methods, system, and non-transitory processor-readable storage medium for higher-level service health are provided herein. An example method includes monitoring, by a monitoring tool, status metrics associated with at least one of a plurality of physical devices and a plurality of component services, where alerts generated from the monitoring tool are stored in a database associated with the monitoring tool. At least one of the component services executes on the at least one of the plurality of physical devices. A microservice identifies at least one logical service. The logical service is comprised of at least one of the plurality of physical devices, and/or at least one of the plurality of component services. The microservice determines a health metric associated with at least one logical service based on the generated alerts. The microservice transmits the health metric associated with the logical service from the microservice to the monitoring tool.

Abstract: Systems and techniques for multi-factor cloud service storage device error prediction are described herein. A set of storage device metrics and a set of computing system metrics may be obtained. A feature set may be generated using the set of storage device metrics and the set of computing system metrics. Members of the feature set may be validated by evaluating a validation training dataset using the members of the feature set. A modified feature set may be created based on the validation. A storage device failure model may be created using the modified feature set. A storage device rating range may be determined by minimizing a cost of misclassification of a storage device. A set of storage devices to be labeled may be identified as having a high probability of failure.

Abstract: Provided are a method, apparatus and system for storing fault data, an embodiment of the fault data storage method including: acquiring fault data of a target electrical device; sending a consensus request for the fault data, usable to request that consensus personnel who use the consensus client reach a consensus for the reason why the target electrical device is faulty, to at least one consensus client; respectively receiving a consensus result from each consensus client, the consensus result being formed by the consensus client according to the triggering of the consensus personnel; determining, according to each received consensus result, whether the reason for why the target electrical device is faulty is due to device quality; generating, upon the reason for why the target electrical device is faulty being due to device quality, a first data block which contains the fault data; and storing the first data block into a blockchain.

Abstract: A method and system includes generating event logs, storing in a database log data comprising the generated event logs, identifying a first set of log data by searching the log data for a first log data type, the first log data type having a data structure that includes a generic identifier and a specific data, identifying pairs of generic identifiers and associated specific data in the first set of log data, for each generic identifier of the identified pairs identifying a second set of log data that include the generic identifier but not the specific data, wherein identifying the second set of log data is performed by searching the log data for event logs that include the generic identifier, and updating log data stored in the database by adding, for each of the event logs of the identified second set of log data, the specific data associated with the generic identifier.

Abstract: A method for detecting comprehensive GPU-related factors of a distributed cluster, the method including: (1): checking whether there is a configuration file content of an operating node; (2): reading a mode parameter in an environment variable of the operating node, and correspondingly switching an operating mode according to the mode parameter; (3): reading a timer frequency value from the environment variable of the operating node so as to set a time period for reading a GPU information parameter according to the timer frequency value; (4): calculating the maximum value of the GPU information parameter of the operating node, and storing the maximum value into the GPU information list cache; and (5): initializing the transmitted information; determining whether there is a GPU in the GPU information list cache of the operating node.

Abstract: A method for billing server utilization based on power includes receiving power consumption data of a remote server used by a customer, deriving a power-utilization correlation between power consumption of the remote server with utilization of the remote server, determining utilization of the remote server from the power consumption data and the power-utilization correlation, preparing a bill for the customer based on the determined utilization of the remote server, and sending the bill to the customer.

Abstract: Screen response validation of robot execution for robotic process automation (RPA) is disclosed. Whether text, screen changes, images, and/or other expected visual actions occur in an application executing on a computing system that an RPA robot is interacting with may be recognized. Where the robot has been typing may be determined and the physical position on the screen based on the current resolution of where one or more characters, images, windows, etc. appeared may be provided. The physical position of these elements, or the lack thereof, may allow determination of which field(s) the robot is typing in and what the associated application is for the purpose of validation that the application and computing system are responding as intended. When the expected screen changes do not occur, the robot can stop and throw an exception, go back and attempt the intended interaction again, restart the workflow, or take another suitable action.

Abstract: A system for detecting errors and correcting errors in a multi-thread processor is disclosed. The multi-thread processor includes a first processor and a second processor. First processor executes a first thread and a second thread. Second processor executes a third thread and fourth thread. An instruction execution is initiated in all four threads. Output of the instruction execution from all four threads are compared for a match by a data compare engine to detect an error in execution of the instruction. When output of the instruction execution from one of the four threads does not match, an error in execution is detected and the output is replaced by one of the other three threads whose output does match. When output of the instruction execution by two or more threads does not match, error is detected, but not corrected.

Abstract: An error notification system includes a plurality of data production systems in communication with a monitoring server. Each data production system has a data processor configured to receive input data from a first set of data production systems, process the input data to produce output data, and make the output data accessible to a second set of data production systems. The monitoring server is configured to monitor data transmissions between the data production systems and to identify, for each data transmission, originating and receiving systems. The monitoring server is further configured to map data flow from each originating source system to identify all downstream data production systems. Upon identification of a data error in the originating source system, the monitoring server obtains data error information, assembles a data error notification, and transmits the data error notification to data production systems meeting system notification criteria.

Abstract: Example aspects include techniques for real-time detection of metric baseline behavior change. These techniques may include generating a reference distance signature based on historic time series information for a component metric, the historic time series information corresponding to a first period of time, generating a sample distance signature based on sample time series information for the component metric, the sample time series information corresponding to a second period of time, and comparing the reference distance signature to the sample distance signature to determine a signature difference. In addition, the techniques may include determining that the second period of time is a baseline change candidate based on the signature difference being greater than a distance threshold, and presenting, based at least in part on the signature difference, an alert notification identifying the second period of time as the baseline change candidate.

Abstract: An apparatus is disclosed in which the apparatus may include a plurality of cores, including a first core, a second core and a third core, and circuitry coupled to the first core. The first core may be configured to process a plurality of instructions. The circuitry may be may be configured to detect that the first core stopped committing a subset of the plurality of instructions, and to send an indication to the second core that the first core stopped committing the subset. The second core may be configured to disable the first core from further processing instructions of the subset responsive to receiving the indication, and to copy data from the first core to a third core responsive to disabling the first core. The third core may be configured to resume processing the subset dependent upon the data.

Abstract: A computer system is provided. The computer system includes a memory and at least one processor coupled to the memory. The at least one processor is configured to scan session data representative of operation of a user interface comprising a plurality of user interface elements; detect, at a point in the session data, at least one changed element within the plurality of user interface elements; classify, in response to detecting the at least one changed element, the at least one changed element as either indicating or not indicating an error; store an association between the error and the point in the session data; and provide access to the point in the session data via the association.