In an example embodiment, a workflow engine is introduced within a cloud landscape. Runbooks re then implemented as workflow templates within the workflow engine. The workflow engine allows for creation of workflows from the workflow templates as well as composing workflows from individual steps. The workflow engine provides a mechanism to describe workflow templates and workflow sets as code.

Systems and methods are presented for selection of compatible components for an observed system. An exemplary method comprises collecting parameters of one or more components of the system, assessing conformity of the one or more components of the system with a required state of the system, identifying one or more anomalies based on the assessment of conformity, analyzing the one or more anomalies to identify a class and parameters of the system corresponding to the one or more anomalies, determining one or more models of methods of restoration of the system, selecting one or more components that meets requirements of the one or more models of methods of restoration and implementing the one or more components in the system that are compatible with the system to eliminate the one or more anomalies.

A system including a data repository storing metrics describing operational behavior of software programs executing in an enterprise system. The system also includes an application programming interface (API) gateway configured to receive the metrics. The system also includes an ingestion layer configured to ingest the metrics to form the ingested metrics. The system also includes a tumbling window processor configured to process the ingested metrics and the events into heat maps, sort the heat maps into the time slices, and populate the time slices with the ingested metrics.

According to an aspect, a method includes searching for a correlated log identifier in a correlation database based on detecting a metrics-based anomaly. The method also includes providing, in a problem diagnosis, related log information associated with the correlated log identifier based on locating one or more log entries including the correlated log identifier in a same time window as the metrics-based anomaly. The method further includes searching for a correlated metric in the correlation database based on detecting a log-based anomaly and providing, in the problem diagnosis, related metric information associated with the correlated metric based on locating one or more metrics records including the correlated metric in the same time window as the log-based anomaly.

Artificial intelligence (AI) model monitoring and ranking includes obtaining metric values indicative of performance of AI model deployments, the metric values including respective metric values measured across metrics, determining violation statuses of the metrics for each of the AI model deployments, the violation statuses indicating, for each AI model deployment, which of the metrics are violated by the AI model deployment as reflected by respective metric values for that AI model deployment, ranking the AI model deployments against each other according to a ranking model and based on the determined violation statuses for each of the AI model deployments, and providing a rank of at least some of the AI model deployments to a user.

A method for use by a primary device associated with a secondary device of a redundant pair, the primary device issuing a synchronization request to its control database causing the primary device to send a tracked memory file storage of the primary device to the secondary device to update the secondary device control database and to periodically send on request of the primary device the cached changes made in the primary device to the secondary device to update the secondary device control database. The secondary device using the updated control database to identify communications connections and paths to I/O modules and peer devices assigned to the secondary device and to perform diagnostic testing of the communications connections and paths identified by the interrogation and send diagnostic messages upon detection of faults in the communication connections and paths identified.

A method and system for recording and logging errors in a computer system includes reading first error handling information with respect to a transaction. The first error handling information is stored in a first component, and based upon a condition of the storage in the first component, an oldest error information is evicted from the first component.

A method for detecting an abnormal occurrence of an application program includes a feature parameter collected according to the log data of at least one application program. The feature parameter is inputted into a first and a second prediction model and a first and a second detection model, and the feature parameter is calculated based on the first and the second prediction model and the first and the second detection model to respectively generate a first and a second prediction value and a first and a second detection value. The first and the second prediction value and the first and the second detection value are respectively weighted based on an abnormal score evaluation equation to generate an abnormal evaluation value of the application program. Finally, the abnormal evaluation value is inputted into a warning ranking model to rank the abnormal evaluation value, generating the corresponding warning signal.

Clustering structured log data by key-values includes receiving, via a user interface, a request to apply an operator to cluster a set of raw log messages according to values for a set of keys associated with the request. At least a portion of each raw log message comprises structured machine data including a set of key-value pairs. It further includes receiving a raw log message in the set of raw log messages. It further includes determining whether to include the raw log message in a cluster based at least in part on an evaluation of values in the structured machine data of the raw log message for the set of keys associated with the request. The cluster is included in a plurality of clusters. Each cluster in the plurality is associated with a different combination of values for the set of keys associated with the request. It further includes providing, via the user interface, information associated with the cluster.

Methods, systems, and devices for detecting page fault traffic are described. A memory device may execute a self-learning algorithm to determine a priority size for read requests, such as a maximum readahead window size or other size related to page faults in a memory system. The memory device may determine the priority size based at least in part on by tracking how many read requests are received for different sizes of sets of data. Once the priority size is determined, the memory device may detect subsequent read requests for sets of data having the priority size, and the memory device may prioritize or other optimize the execution of such read requests.