Techniques are disclosed relating to automatically resolving an error in a user interaction with a user page without the user having to disengage from the user page to resolve the error. A monitoring agent may interface with the user page. The monitoring agent may provide an error signal to an error resolution module in response to detecting an error in the user interaction with the user page. The error resolution module may determine a causal factor for the error based on the error signal and contextual data at the time of the error. A resolution flow may be determined based on the causal factor. The resolution flow may be implemented by the monitoring agent contextually within the user page to resolve the error without the user disengaging from the user page.

Machine-learning processing of aggregate data including record-size data to predict failure probability is described herein. In an example, a system identifies electronic data that is longitudinal and includes a set of electronic records pertaining to a given subject or to a given object. The system generates a record-size metric that characterizes a size of the electronic data and determines a physical attribute of the given subject or the given object. The system generates a physical-attribute metric based on the physical attribute, generates an input data set that includes the record-size metric and the physical-attribute metric, and generates a failure probability across a given time period and for the given subject or the given object by processing the input data set using a trained machine-learning model. The system determines that an alert condition is satisfied based on the failure probability and outputs an alert representing the failure probability.

A computer-implemented method includes feeding exception log entries from a plurality of exception logs associated with respective heterogenous computing components into an aggregated exception log comprising aggregated exception log entries, mapping an aggregated exception log entry in the aggregated exception log to a corresponding exception identifier, identifying a descriptor describing an exception condition based on the exception identifier, determining one or more destinations corresponding to the aggregated exception log entry, and routing a notification comprising the descriptor to the one or more destinations.

A system for estimating one or more data storage parameters and/or statistics in a data storage system is presented. The data storage system includes a plurality of storage containers. The system includes a snapshot module, a container stats aggregator, a synchronization module, a global stats aggregator, and storage stats estimator.

Systems and methods for enhancing the representation of outliers in a distribution of telemetry data of a monitored system are provided. According to one embodiment, telemetry data of the monitored system may be continuously collected. Frequency values representing a frequency of occurrence of corresponding telemetry data of the collected telemetry data may be generated by aggregating the collected telemetry data. As the vast majority of telemetry data is expected to represent a normal operating state of the system and relatively few, if any, of the telemetry data (e.g., outliers) will be indicative of one or more events of significance, the resulting distribution of the frequency values is highly skewed. In order to facilitate visualization of the distribution that accentuates the outliers, display characteristics may be calculated for the frequency values by applying a visualization model based on a weighted combination of multiple data transformations to each of the frequency values.

The present invention extends to methods, systems, and computer program products for deriving unified insights ad logs from DevOps CI/CD tools and pipeline data. In general, a data transformer facilitates data normalization and serialization converting raw data across multiple DevOps tools and stores the data into a Data Lake in accordance with a customized schema. A continuous orchestrator sequences, aggregates and contextualizes the logs, providing an intuitive way of troubleshooting issues across a DevOps environment, historical data for compliance and audit purposes, and a build manifest for root cause analysis. The continuous orchestrator also processes the logs and leverages a KPI framework, providing intelligent dashboards across 90+ KPI's and a plurality of different dimensions (Planning, Development/pipelines, security, quality, operations, productivity and source code) to help customers make smart decisions and do more with less.

Monitoring a performance of one or more computing systems includes configuring, by at least one processor, a sampling window for sampling exceptions data generated by at least one application instance being executed by a computing device, the exceptions data indicating an occurrence of at least one operation performed by the application instance. Configuring the sampling window comprises determining a number of exceptions generated for a prior sampling window, determining a computing bandwidth that is available for monitoring the exceptions, and controlling a sampling rate of the sampling window based on the number of exceptions and the computing bandwidth. The monitoring includes receiving, during the sampling window, the exceptions data generated by the at least one application instance, sampling, based on the sampling rate, the exceptions data, and generating, based on the sampling, summary data representing the exceptions data.

Performance of a collection of machines, arranged in a linear sequence of machines that form a linear communication orbit (LCO), is monitored. Multiple machines in the LCO receive, via the LCO, a set of rules (or various subsets of the same set of rules), each rule specifying one or a combination of conditions (e.g., a performance metric and corresponding criterion) for satisfying the rule, evaluate those rules with respect to locally occurring events and local processes, and stores results of those evaluations in a local database. In response to a performance query sent to the machines via the LCO, each of the machines returns a report, including information identifying processes whose performance during the specified time period satisfies at least one rule in the set of one or more rules. Those reports are aggregated and used to present performance information to a user.

A first and a second control modules of a control apparatus of the present disclosure mutually monitor a state of the other end and send a reset request signal to a monitoring module when the other end should be reset. The monitoring module sends a reset signal to one of the first and second control modules when the monitoring module receives the reset request signal indicating that the one of the first and second control modules should be reset from the other and the monitoring module does not send the reset signal to the other. The monitoring module prohibits a reset of one of the first and second control modules when the monitoring module receives the reset request signal indicating that the one of the first and second control modules should be reset from the other and the monitoring module sends the reset signal to the other.

According to some embodiments, a system and methods comprising receiving application code for an on-premise application at a custom code cloud enabler module, wherein the application code includes at least one package of a plurality of objects; providing a whitelist of a plurality of cloud elements for the plurality of objects; identifying a first enhancement point in a first application object of the plurality of objects, the first enhancement point including a first extension element; selecting a first cloud element from the whitelist of cloud elements; determining the selected first cloud element matches a structure definition of the first extension element; mapping one or more parameters of the first extension element to one or more parameters in the matched first cloud element generating a cloud code snippet for the first extension element based on the mapping; and executing the generated cloud code snippet for the first enhancement point as part of the cloud code on a cloud platform. Numerous other aspects are provided.