Abstract: A data stream transformer maps a source data stream model of a source application to a set of one or more destination models of consumer applications. Each destination model is described using a JSON schema. A configuration YAML file having one or more templates is created, with each template being based on the JSON schema and defining methods that the data stream transformer uses to map the source data stream model into the respective destination model. The JSON schema defining the destination model is compiled to create a set of destination model classes. The source application creates source objects forming the source data stream. The data stream transformer uses reflection to retrieve the values of the source objects and uses the template and destination model classes to build destination model objects based on the values of the source model objects. The destination model objects are output to the consumer application.

Abstract: A multi-site data storage monitoring system communicates executable actions to remote element managers in non-executable text files. Each non-executable text file includes a human-readable textual description of the action, a top-level Java class of a known task type, and essential properties. The element managers generate executable jobs from the non-executable text files by instantiating an empty task object based on the top-level Java class, partially populating the empty task object based on the essential properties, adding the partially populated task object to a job structure, and using a token to provide authorization at job run-time. The token is created using user authentication information provided in response to presentation of the human-readable textual description of the action.

Abstract: Current data from a SaaS system and associated remote data storage system is used to automate proactive fault avoidance. Machine learning models are used to predict faults. Rules are used with a rules engine to calculate corresponding fault avoidance recommendations. The model and rules are trained and created using source data from the SaaS system and remote data storage system with which the model and rules will be used. Current data from those systems is used to predict future faults and calculate recommendations to avoid the predicted faults. Implementation of a recommendation triggers a re-test to determine whether the predicted fault is still likely to occur.

Abstract: An example methodology includes, by a computing device, determining a recurring time period during which performance data breaches a performance data threshold and applying a data exclusion window on the recurring time period, wherein performance data within the data exclusion window are excluded from system performance calculations. The method also includes, by the computing device, retrieving historical performance data associated with the data exclusion window and determining a percentage of the historical performance data that breach the performance data threshold. The method further includes, responsive to a determination that the percentage of the historical performance data that breach the performance data threshold is within an acceptable breach threshold, removing, by the computing device, the data exclusion window on the recurring time period.

Abstract: A cause analysis and remediation engine (CARE) can perform an analysis of inputs characterizing aspects of the system at the time an alert is generated in order to determine causes and remediation actions for the alert. The CARE can automatically detect and analyze inputs including configuration information, compliance information, and workload and performance information to determine one or more expected causes of the alert and one or more corresponding remediations that can be taken. The CARE can include a rules engine and a remediation service. The rules engine can include one or more trained models, such as trained machine learning models, each using rules to recognize inputs denoting a particular expected cause of the alert. The models can be decision trees trained using supervised learning. The remediation service can map one or more expected causes determined by the rules engine to the one or more corresponding remediations.

Abstract: A system, method, and computer-readable medium 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; identifying a plurality of data center issues based upon the monitoring; performing an artificial intelligence operations assisted data center monitoring and management operation, the artificial intelligence operations assisted data center monitoring and management operation data center issue grooming operation automating at least one of identification and resolution of a particular data center issue; and, presenting information regarding the at least one of identification and resolution of the data center issue.

Abstract: A system, method, and computer-readable medium 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; identifying a plurality of data center issues based upon the monitoring; performing an artificial intelligence operations assisted data center monitoring and management operation, the artificial intelligence operations assisted data center monitoring and management operation data center issue grooming operation automating at least one of identification and resolution of a particular data center issue; and, presenting information regarding the at least one of identification and resolution of the data center issue.

Abstract: A system, method, and computer-readable medium 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 of a customer; identifying a plurality of data center issues based upon the monitoring; performing an artificial intelligence operations assisted data center monitoring and management operation, the artificial intelligence operations assisted data center monitoring and management operation automating at least one of identification and resolution of a particular data center issue; generating information regarding the at least one of identification and resolution of a particular data center issue; and, using the information regarding the at least one of identification and resolution of a particular data center issue to facilitate adoption of the artificial intelligence operations assisted data center monitoring and management operation by the customer.

Abstract: A cause analysis and remediation engine (CARE) can perform an analysis of inputs characterizing aspects of the system at the time an alert is generated in order to determine causes and remediation actions for the alert. The CARE can automatically detect and analyze inputs including configuration information, compliance information, and workload and performance information to determine one or more expected causes of the alert and one or more corresponding remediations that can be taken. The CARE can include a rules engine and a remediation service. The rules engine can include one or more trained models, such as trained machine learning models, each using rules to recognize inputs denoting a particular expected cause of the alert. The models can be decision trees trained using supervised learning. The remediation service can map one or more expected causes determined by the rules engine to the one or more corresponding remediations.

Abstract: A storage system management application receives reports describing operation of a storage system from a performance monitoring system, and compares the storage system operating metrics against a set of respective thresholds. If the metrics breach one or more thresholds, the seasonality boundaries for the respective metric are calculated, and an alert is only generated if the storage system operating metric is both outside the performance threshold and outside of the calculated seasonality boundaries. By determining the seasonality boundaries and using the seasonality boundaries to determine when to generate alerts, it is possible to reduce the number of alerts generated. By including the seasonality values in the alerts, the deviation from normal seasonably operating conditions can be determined from the alert, thus enabling a person who receives the alert to determine the magnitude of deviation of the storage system operation from seasonably adjusted normal operation.

Abstract: A method for managing data includes obtaining, by a data processor, a time bucket calculation request, and in response to the time bucket calculation request: obtaining a plurality of data points from a database based on a period of time, wherein the period of time is specified in the time bucket calculation request, processing each data point in the plurality of data points based on a timestamp of each data point and a data point interval, generating a plurality of time buckets based on the processing, and storing the plurality of time buckets in the database.

Abstract: A storage system management application receives reports describing operation of a storage system from a performance monitoring system, and compares the storage system operating metrics against a set of respective thresholds. If the metrics breach one or more thresholds, the seasonality boundaries for the respective metric are calculated, and an alert is only generated if the storage system operating metric is both outside the performance threshold and outside of the calculated seasonality boundaries. By determining the seasonality boundaries and using the seasonality boundaries to determine when to generate alerts, it is possible to reduce the number of alerts generated. By including the seasonality values in the alerts, the deviation from normal seasonably operating conditions can be determined from the alert, thus enabling a person who receives the alert to determine the magnitude of deviation of the storage system operation from seasonably adjusted normal operation.

Abstract: A method for managing data includes obtaining, by a data processor, a time bucket calculation request, and in response to the time bucket calculation request: obtaining a plurality of data points from a database based on a period of time, wherein the period of time is specified in the time bucket calculation request, processing each data point in the plurality of data points based on a timestamp of each data point and a data point interval, generating a plurality of time buckets based on the processing, and storing the plurality of time buckets in the database.