Abstract: An embodiment of the present invention is directed to a novel approach of applying Machine Learning, statistical methods and/or other algorithms to identify associations of input conditions and values with results of requirements, measures of performance assessments, and/or other indications. These associations may be provided to an analyst, system designer, other recipient and/or receiving system or component to inform of input conditions and values that uncover system sensitivities.

Abstract: Automated processes and systems troubleshoot and optimize performance of applications running in distributed computing systems. An automated computer-implemented processes train an inference model for an application based on metrics associated with the application and a key performance indicator (“KPI”) of the application. When a run-time performance problem is detected in run-time KPI values of KPI, the trained inference model is applied to run-time metrics and run-time KPI values to identify relevant run-time metrics that can be used to identify the root cause of the performance problem. The root cause of the performance problem can be used to generate a recommendation for correcting the performance problem. An alert identifying the root cause of the performance problem and the recommendation for correcting the performance problem are displayed on an interface of a display, thereby enabling correction of the performance problem and optimization of the application.

Abstract: Some embodiments of the present invention are directed towards techniques for validating performance degradation of cloud deployed application from neighbor based variability. Historical runs of an application deployed in a cloud environment are received. In these embodiments, a subset of these historical runs, using associated performance metrics recorded during the historical runs, are compared against performance metrics of a current version of the application which is deployed in a cloud environment to determine a subset of historical runs similar to the current version. The determined subset is then used to draw comparisons with performance metrics of a baseline run of the application to validate if a performance degradation has occurred by updating the application to the current version, reducing the impact of neighbor-based variability on evaluating performance degradation.

Abstract: A process for analyzing an incident includes setting up an alert for a high error rate on a particular endpoint. Once the alert is triggered, a set of traces for transactions exhibiting errors on the offending endpoint is queried. All traces for other services/operations that include errors on the offending endpoint are also enumerated. A set of baseline transactions that involve the offending endpoint, but do not result in error may be utilized to determine whether the errors are always present, or are distinctive for certain offending transactions. All traces are ranked based on a statistic. Once the traces have been ranked, they may be traced down to a deepest/most terminal error. A set of transactions that correlate to the terminal error may also be analyzed to determine infrastructure causes.

Abstract: The present disclosure relates to system and techniques for prediction of failures in resources deployed in a data plane of a cloud based infrastructure. The resource are selected from a plurality of cloud based resources arranged in a hierarchical manner and allocated to a client device. A predictor employs a first prediction model to obtain a first prediction of a failure of a resource, and a second prediction model to obtain a second prediction of the failure of the resource. Weights are assigned to the first prediction and second prediction based at least in part on a criterion. The predictor computes an overall prediction of the failure of the resource based at least in part on at least one of the first prediction, the second prediction or the respective weights assigned to the predictions. The overall prediction is utilized for restoring the failure of the resource.

Abstract: Implementations described herein relate to methods, systems, and computer-readable media to identify anomalous applications. In some implementations, the methods may include obtaining application metric data that includes application level metrics and instance level metrics for each instance of a plurality of instances associated with a respective application of a plurality of applications operating over a distributed computing system, generating a first anomaly detection score based on the instance level metrics; generating a second anomaly detection score based on one or more input metrics associated with the respective application, generating a third anomaly detection score based on seasonal metric data associated with the respective application and identifying at least one application of the plurality of applications as an anomalous application based on the first anomaly detection score, the second anomaly detection score, and the third anomaly detection score.

Abstract: A system includes a computing platform communicatively coupled to multiple network nodes, the computing platform having processing hardware and a memory storing a software code. The processing hardware is configured to execute the software code to detect multiple anomalous performance indicators originating from one or more of the network nodes, determine, using the anomalous performance indicators in an automated process, the occurrence of an incident, and determine the signature of the incident. The processing hardware is further configured to execute the software code to compare the signature to one or more entries in an incident signature database, perform, when the comparison determines that the signature corresponds to one or more of the entries, a root cause analysis of the incident using the corresponding one or more entries, and generate an incident alert including one or both of a result of the root cause analysis and a description of the incident.

Abstract: An information processing apparatus includes: a reception module configured to receive selection of an output signal corresponding to a root cause from a group including a plurality of output signals, the plurality of output signals being obtained from a plurality of devices to be managed and being obtained in a predetermined period; a replacement module configured to replace a selection result received by the reception module with a combination of the selection results in two output signals, the selection result being corresponding to the group, the two output signals being selected from the plurality of output signals; and a model generation module configured to generate a ranking model of the plurality of output signals based on the selection results after the replacement.

Abstract: There is provided a system and method of analysing anomalies in one or more electronic appliances including at least one computer. The method comprises, by a processor and memory circuitry, upon detection of a deviation of a given parameter representative of the one or more electronic appliances from an operational state, providing a model associated with the given parameter, wherein the model links one or more other parameters to the given parameter, wherein the one or more other parameters affect the given parameter, and based at least on the model, identifying, among the one or more other parameters, at least one parameter Pj for which a change in its value allows bringing back the given parameter to the operational state.

Abstract: A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: monitoring a plurality of data center assets contained within a data center; receiving a data center asset data stream, the data center asset data stream comprising a plurality of data center events, at least some of the plurality of data center events having associated data center issues; capturing asset alert data from the plurality of data center events; and, performing a data center issue analysis operation, the data center issue analysis operation using the asset alert data from the plurality of data center events, the data center issue analysis operation identifying a data center asset from the plurality of data center assets affected by the particular data center issue.

Abstract: The disclosed technology provides resource locators keyed to resource names with failover alternate resource locators keyed to resource positions on user interfaces in a test environment: providing a central repository for resource locators and storing a set of alternative locator expressions for a resource in the central repository. The set includes first resource locators keyed to resource names on UIs and respective second resource locators keyed to resource positions on UIs that can be evaluated when the resource name in the respective first resource locator is invalid. The method invokes a resource location navigator to locate a particular resource using a first resource locator keyed to a resource name on the user interface, automatically invoking the resource location navigator using the second resource locator keyed to the resource position after failure of locating the particular resource using the resource name, and accessing the particular resource using the second resource locator.

Abstract: A computer system configured to identify errors in a session launch initiated by a client application 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 receive one or more events from one or more applications or devices involved in the session launch, wherein an event of the one or more events comprises information from an application or device call (e.g., an application programming interface (API) call) communicated during the session launch, the information comprising destination information; build a primary Directed Acyclic Graph (DAG) based on the information from the API call; determine an error identifier based on the primary DAG; retrieve a troubleshooting recommendation from a library based on the error identifier; and send the troubleshooting recommendation to the client application.

Abstract: A method includes for each node of a plurality of nodes, determining a runtime value associated with an execution of a continuous integration and continuous delivery/deployment (CI/CD) pipeline, determining, for each node of the plurality of nodes, an aggregate performance statistic in view of the runtime value of the plurality of nodes, identifying, in view of the aggregate performance statistic, at least one node of the plurality of nodes that satisfies a performance threshold criterion, and responsive to identifying the at least one node of the plurality of nodes that satisfied the performance threshold criterion, marking the at least one node of the plurality of nodes as underperforming.

Abstract: A memory fault handling system includes: a memory controller, firmware, and software. The memory controller has: an error occurrence notification means for, when the occurrence of a memory error is detected, notifying firmware or software of the occurrence of the memory error on the basis of a setting pertaining to a notification recipient. The firmware includes: a notification recipient setting means for setting a notification recipient; and a spare memory switching instruction means for, when a notification indicating the occurrence of a memory error is received from the memory controller, instructing to switch from a memory to a spare memory. The software includes: a disabling instruction means for, when a notification indicating the occurrence of a memory error is received from the memory controller, instructing to cause a page corresponding to the memory in which the memory error has occurred to be disabled.

Abstract: A system and method of automated downtime scheduling and control is disclosed. A failure of at least one component of at least one bare-metal server associated with a client is detected and a first notification is transmitted to a client system associated with the client. The notification includes a request to schedule downtime. A response including a selected downtime is received from the client system and the at least one bare-metal server is transitioned to an offline state at the selected downtime. A ticket is generated in a ticketing system for repair of the bare-metal server. The ticket identifies the at least one component.

Abstract: Techniques are disclosed for capturing diagnostics data in a distributed computing environment comprising a plurality of computing devices executing a plurality of Kubernetes pods. A worker node is configured with a staging area for storing temporary diagnostics data. An agent is configured to upload the temporary diagnostics data. Each container in the worker node is assigned a directory in the staging area for writing the container's temporary diagnostics data. When a container in the worker node has written a temporary diagnostics data file to the container's directory in the staging area, the temporary diagnostics data file is uploaded to the persistent storage.

Abstract: Systems and methods for performance monitoring of distributed ledger nodes by data intake and query systems. An example method includes: receiving, by an application performance monitoring engine, from a distributed ledger node, values of a plurality of metrics reflecting operational parameters of one or more tasks performed by the distributed ledger node; determining, by analyzing a data set comprising the values of the plurality of metrics, a value of a performance parameter of the distributed ledger node; and generating an alert responsive to determining that the value of the performance parameter satisfies an alert triggering condition.

Abstract: Embodiments of the present disclosure provide improved identification and handling of root causes for data event(s). Some embodiments improve the accuracy of determinations of a root cause or likely order of root causes of a data event affecting any number of system(s), and cause transmission of data associated with such root cause(s) for use in triaging such data event(s) and/or facilitating efficient servicing to resolve the data event. Some embodiments utilize modified centrality algorithm(s) to efficiently and accurately identify a likely root cause of a data event in a computing environment. Some embodiments generate and/or output notifications that indicate the particular computing system(s) identified as a root cause of a data event, and/or the particular computing system(s) identified not as a root cause but affected by a data event of the root cause computing system.

Abstract: Provided are a computer program product, system, and method for using a track format code in a cache control block for a track in a cache to process read and write requests to the track in the cache. A track format table associates track format codes with track format metadata. A determination is made as to whether the track format table has track format metadata matching track format metadata of a track staged into the cache. A determination is made as to whether a track format code from the track format table for the track format metadata in the track format table matches the track format metadata of the track staged. A cache control block for the track being added to the cache is generated including the determined track format code when the track format table has the matching track format metadata.

Abstract: A system and method for scaling resources of a secondary network for disaster recovery uses a disaster recovery notification from a primary resource manager of a primary network to a secondary resource manager of the secondary network to generate a scale-up recommendation for additional resources to the secondary network. The additional resources are based on latest resource demands of workloads on the primary network included in the disaster recovery notification. A scale-up operation for the additional resources is then executed based on the scale-up recommendation from the secondary resource manager to operate the secondary network with the additional resources to run the workloads on the secondary network.