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.