Abstract: Computer-implemented methods and systems described herein perform intelligent sampling of application traces generated by an application. Computer-implemented methods and systems determine different sampling rates based on frequency of occurrence of trace types and/or frequency of occurrence of durations of the traces. Each sampling rate corresponds to a different trace type and/or different duration. The sampling rates for low frequency trace types and durations are larger than the sampling rates for high frequency trace types and durations. The relatively larger sampling rates for low frequency trace types and low frequency durations ensures that low frequency trace types and low frequency durations are sampled in sufficient numbers and are not passed over during sampling of the application traces. The set of sampled traces are stored in a data storage device.

Abstract: Provided is a watchdog system including: a software area including a bootloader area and an application area, and a shared memory area storing the last watchdog refresh timing; and an external watchdog module refreshing a watchdog when a transmitted refresh request timing corresponds to window open time in response to a refresh request transmitted from either the bootloader area or the application area and based on the last watchdog refresh timing transmitted from the shared memory area and the transmitted refresh request timing.

Abstract: Embodiments of the present disclosure relate to a method, a device and a computer program product for recovering data. The method comprises in response to receiving a request to recover data from a backup node to a source node, determining a plurality of candidate backup nodes having copies of the data; determining respective performance indicators of the plurality of candidate backup nodes; and determining, from the plurality of candidate backup nodes, the backup node for recovering the data to the source node based on the respective performance indicators of the plurality of candidate backup nodes.

Abstract: A method for managing upgrades of components of clients includes obtaining an upgrade failure prediction request associated with a client of the clients, and in response to obtaining an update failure prediction request: obtaining live data associated with the client, matching the live data with a training data cluster, selecting relevant features associated with processed training data of the training data cluster, generating an upgrade failure prediction using the live data associated with the relevant features and a prediction model, making a determination that the upgrade failure prediction implicates an action is required, and based on the determination, initiating performance of the action.

Abstract: A system includes a computer system, a memory, and processor. The computer system includes a plurality of units of system resources, each executing a workload unit. The memory stores a set of remediation scripts. Each remediation script is associated with a known failure of a set of known failures within the computer system. Each remediation script is configured, when executed, to remediate the known failure. The processor measures performance metrics associated with the computer system. The processor determines, based on the performance metrics, that a probability that a failure within the computer system will occur within a future time is greater than a set threshold. In response, the processor determines, based on the values for the set of metrics, that the failure is a known failure, and executes a remediation script associated with the known failure.

Abstract: An extension device is positioned within a point-to-point link to connect two devices, where the extension device includes error detection circuitry to detect a set of errors at the extension device. The extension device further includes memory to store an event register, where the extension device is to write data to the event register to describe detection of an error by the error detection circuitry. The extension device further includes a transmitter to transmit a notification signal to indicate the detection of the error and presence of data in the evert register associated with the error.

Abstract: Techniques are disclosed relating to methods that include initializing, by a computer in a computer system, an event counter that includes a plurality of sub-counter groups, each plurality of sub-counter groups including at least two sub-counters located on different nodes of a plurality of nodes in the computer system. In response to an occurrence of an event associated with the event counter, the method may include the computer selecting a particular sub-counter group of the plurality of sub-counter groups to update, and sending, to sub-counters corresponding to the particular sub-counter group, a request to update a sub-counter value for the particular sub-counter group. In response to a request for a current count value of the event counter, the method may include outputting, by the computer, a sum of the sub-counter values for the plurality of sub-counter groups as the current count value.

Abstract: A self-correcting secure computer system is provided. The computer system includes a read-only memory (ROM) device, a random access memory (RAM) device, and at least one processor in communication with the ROM device and the RAM device. The at least one processor is programmed to receive an activation signal, retrieve, from the ROM device, data to execute an operating system, and execute, on the RAM device, the operating system based on the data from the ROM device.

Abstract: Techniques for causal modeling. Historical feature data is received, relating to a plurality of nodes in a system. A machine learning (ML) model is generated, for a node of the plurality of nodes. The ML model is trained to predict a plurality of future feature values for the node based on at least a portion of the historical feature data. A causal graph is generated, for the plurality of nodes, using a feature selection mechanism within the ML model. The feature selection mechanism includes a regularization term encouraging sparsity of nodes in selected features in the ML model.

Abstract: The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes generating a dependency graph based on one or more microservices, computing, for each microservice of the one or more microservices, a complexity score using the dependency graph, identifying a subset of the one or more microservices, wherein each microservice in the subset of the one or more microservices has a complexity score meeting a threshold value, and applying a transactional lock on each microservice in the subset of the one or more microservices.

Abstract: An anomaly service receives log data from nodes in a computing environment, which includes a sequence of information indicative of log messages produced by the nodes. The anomaly service identifies dominant patterns in the sequence of information that are representative of non-anomalous blocks of the log messages. Having identified the dominant patterns, the service is able to extract the non-anomalous blocks from the log data to reveal anomalous blocks that do not fit the dominant patterns. The service may then generate anomaly vectors based on the anomalous blocks, which can be distributed to the nodes to detect anomalies.

Abstract: A method and an apparatus for generating time series data based on multi-condition constraints, and a medium are provided. The method include the following: a data repair request from a client is received, the data repair request including to-be-repaired data and condition information; normalization processing is performed on the to-be-repaired data to obtain normalized data, and tensor processing is performed on the condition information to obtain a feature label; a trained data repair model is called to perform repair processing on the normalized data according to the feature label to obtain first repaired data, the data repair model being obtained by training according to sample data, a first sample condition, real sample data and a second sample condition, and the sample data being noise data; and the first repaired data is sent to the client.

Abstract: An anomaly service receives log data from nodes in a computing environment, which includes a sequence of information indicative of log messages produced by the nodes. The anomaly service identifies dominant patterns in the sequence of information that are representative of non-anomalous blocks of the log messages. Having identified the dominant patterns, the service is able to extract the non-anomalous blocks from the log data to reveal anomalous blocks that do not fit the dominant patterns. The service may then generate anomaly vectors based on the anomalous blocks, which can be distributed to the nodes to detect anomalies.

Abstract: Described is a system including a server rack comprising a plurality of components, a plurality of touch sensors respectively coupled with the plurality of components of the server rack, and a management system communicatively coupled to the server rack. The management system comprises one or more processors and one or more computer-readable storage media storing instructions, which, when executed by the one or more processors, are configured to cause the management system to perform a method. The method comprises receiving, from the server rack, an indication of a failed component of the plurality of components. The method further comprises receiving, from a first touch sensor of the plurality of touch sensors, a touch indication. The method further comprises transmitting, to the server rack, an indication of whether the first touch sensor is coupled to the failed component.

Abstract: A method for compliance tracking and remediation across a data sharing platform is provided. The method comprises receiving, by a computing system associated with a platform provider, an input of a user to enable a data security tool of the platform provider, enabling, by the computing system, the data security tool, cataloging, by the computing system and via the data security tool, content accessed by the user and associated with an experience provider, detecting, by the computing system, a noncompliance event, the noncompliance event based on content accessed by the user and served by the experience provider that is noncompliant with at least one data sharing preference of the user, and remediating, by the computing system, the detected noncompliance event.

Abstract: A method includes, in response to a memory error indication indicating an uncorrectable error in a faulted segment, associating in a remapping table the faulted segment with a patch segment in a patch memory region, and in response to receiving from a processor a memory access request directed to the faulted segment, servicing the memory access request from the patch segment by performing the requested memory access at the patch segment based on a patch segment address identifying the location of the patch segment. The patch segment address is determined from the remapping table and corresponds to a requested memory address specified by the memory access request.

Abstract: Systems and methods are disclosed including a memory device and a processing device operatively coupled to the memory device. The processing device can perform operations including detecting a read error with respect to data residing in a block of the memory device, wherein the block is associated with a voltage offset bin, determining an ordered set of error-handling operations to be performed to the data, determining a most recently performed error-handling operation associated with the voltage offset bin; adjusting an order of the set of error-handling operations by positioning the most recently performed error-handling operation within a predetermined position in the order of the set of error-handling operations; and performing one or more error-handling operations of the set of error-handling operations in the adjusted order until data associated to the read error is recovered.

Abstract: Techniques for storage management involve: obtaining information indicating an error of a storage device of a data storage system; if the number of occurrences of the error within a predetermined time period exceeds a predetermined threshold, stopping obtaining the information indicating the error; and generating an event indicating whether the number of occurrences of the error within the predetermined time period exceeds the predetermined threshold for further diagnosis of the error. As a result, errors from the storage device can be automatically managed, which helps to improve the data storage system's capacity to handle different types of errors of the storage device.

Abstract: A system includes a first memory device including a non-volatile memory device, a second memory device and a processing device, operatively coupled with the first memory device and the second memory device, to perform operations including configuring a system in accordance with a configuration designating an interface standard for exposing a storage element implemented on the first memory device to a first protocol of the interface standard and a persistent memory region (PMR) implemented on the second memory device to a second protocol of the interface standard, and performing at least one system operation based on the configuration.

Abstract: Techniques for suppressing notifications are disclosed. An incident may repeatedly flap between various resolved and unresolved states. Furthermore, other incident attributes may flap between various states such as, for example, varying levels of incident severity. Each change in state results in the transmission of a notification. In order to reduce the number of notifications, the system estimates a flapping window for the incident based on the flapping behavior of prior incidents. The system computes a notification suppression window based at least in part on the estimated flapping window. The system suppresses notifications corresponding to changes in incident state that are detected during the notification suppression window. The notification suppression window may be extended in response to extending the estimated flapping window.