Abstract: A system for generating a workload model for a target information handling system includes gathering workload data from a plurality of information handling systems, dividing the workload data into a plurality of workload data bins, identifying workload data characteristics for each workload data bin and identifying workload data sets that may be applicable to the target information handling system. A workload mix may be determined based on the target information handling systems and workload data characteristics. Real customer workload data including real-time or near real-time workload data may be used to check a workload model for accuracy before deploying the workload model to a target information handling system.

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: Methods, apparatus, and processor-readable storage media for determining storage system configuration recommendations based on vertical sectors and size parameters using machine learning techniques are provided herein.

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: Methods, apparatus, and processor-readable storage media for automatically adjusting storage system configurations in a storage-as-a-service environment using machine learning techniques are provided herein. An example computer-implemented method includes obtaining performance-related data for at least one storage system in a storage-as-a-service environment; processing at least a portion of the obtained performance-related data using one or more rule-based analyses; identifying, based at least in part on results of the processing, one or more storage system configurations, of the at least one storage system, requiring adjustment; determining, using at least one machine learning technique, one or more adjustment amounts for the one or more storage system configurations; and automatically adjusting the one or more storage system configurations, within the storage-as-a-service environment, in accordance with the one or more determined adjustment amounts.

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: Methods, apparatus, and processor-readable storage media for machine learning-based anomaly detection using time series decomposition are provided herein. An example computer-implemented method includes processing, via machine learning techniques pertaining to time series decomposition functions, a first set of historical time series data derived from multiple systems within an enterprise; generating, based on the processed data, one or more pairs of upper bounds and lower bounds directed to system metrics; identifying system anomalies attributed to one or more of the multiple systems within the enterprise by comparing a second set of historical time series data derived from the one or more systems against the one or more pairs of upper bounds and lower bounds; prioritizing, via machine learning techniques pertaining to weighting functions, the system anomalies; and outputting, in accordance with the prioritization, the system anomalies to a user within the enterprise.

Abstract: Methods, apparatus, and processor-readable storage media for automatically determining storage system data breaches using machine learning techniques are provided herein. An example computer-implemented method includes configuring a storage system by designating at least one storage object within the storage system for storing data identified as to be protected from breach; generating at least one multivariate data breach probability function using historical performance data of the designated storage object(s) and/or historical capacity data of the designated storage object(s); calculating at least one data breach score using the at least one multivariate data breach probability function, one or more machine learning techniques, and additional performance data of the designated storage object(s) and/or additional capacity data of the designated storage object(s); and performing one or more automated actions based at least in part on the at least one data breach score.

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: Systems and methods are provided for providing recommended resolutions to issues occurring during operation of an Information Handling System (IHS). Similar issues, each associated with resolution information specifying one or more recommended resolutions, are identified within an issue repository. Collaborative filtering is utilized on the similar issues and associated resolution information to generate a first ranked list. Collaborative filtering is also utilized on a subset of the similar issues and associated resolution information to generate a second ranked list of the subset of similar issues and associated resolution information, where the subset of similar issues are each selected based on being within a time proximity to the reported issue. A linear regression is utilized on the first ranked list and the second ranked list to generate a ranked list of recommended resolutions to the reported issue that is then provided to an administrator.

Abstract: Methods, apparatus, and processor-readable storage media for automatically processing storage system data and generating visualizations representing differential data comparisons are provided herein. An example computer-implemented method includes obtaining current data from a first storage system and historical data from the first storage system and/or one or more additional storage systems; determining, for the first storage system, at least one current state value for at least one storage system parameter by processing the current data using a first hashing algorithm; determining, for the first storage system with respect to the first storage system and/or the additional storage systems, at least one differential state value for the at least one storage system parameter by processing the current data and the historical data using a second hashing algorithm; and generating data visualizations based on the current state value(s) and/or the differential state value(s).

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: identifying a plurality of assets within a data center; monitoring usage of the plurality of assets within the data center; generating data center asset profile data based upon the monitoring; identifying a plurality of asset configurations related to the asset profile data; ranking the plurality of asset configurations based upon the data center asset profile data; and, generating a recommended asset configuration recommendation based upon the ranking.

Abstract: Methods, apparatus, and processor-readable storage media for determining similarity between time series using machine learning techniques are provided herein. An example computer-implemented method includes obtaining a primary time series and a set of multiple candidate time series; calculating, using machine learning techniques, similarity measurements between the primary time series and each of the candidate time series; for each of the similarity measurements, assigning weights to the candidate time series based on similarity to the primary time series relative to the other candidate time series; generating, for each of the candidate time series, a similarity score based on the weights assigned to each of the candidate time series across the similarity measurements; and outputting, based on the similarity scores, identification of at least one candidate time series for use in one or more automated actions relating to at least one system.

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: identifying a plurality of assets within a data center; generating data center asset profile data for each of the plurality of assets within the data center; identifying a plurality of asset configurations related to the asset profile data; and, calculating costs associated with the plurality of asset configurations related to the asset profile data.

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: identifying a plurality of assets within a data center; monitoring usage of the plurality of assets within the data center; generating data center asset profile data based upon the monitoring; identifying a plurality of asset configurations related to the asset profile data; ranking the plurality of asset configurations based upon the data center asset profile data; and, generating a recommended asset configuration recommendation based upon the ranking.

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: 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: collecting historical asset data; producing a prediction model associated with the historical asset data; identifying semantic changes in telemetry of the historical asset data; 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; identifying telemetry changes and events of interest for the plurality of data center events; and, determining a context of changes for the plurality of data center events, the determining using the prediction model associated with the historical asset data.

Abstract: A methods for identifying a multi-tenant storage array for an application workload includes identifying workload parameters and defining a plurality of groups for each parameter and a plurality of “bins” corresponding to tuples of the groups. Exemplary workload parameters include a percent read parameter and an I/O size parameter. A bin mix of the workload is determined based on historical data wherein the bin mix indicates bins associated with workload activity exceeding a specified threshold. The bin mix is used to define at least some inputs for a supervised learning model of a process for homing application workloads in a multi-tenant storage array. After appropriate training of the model with a generative adversarial network, the model may be invoked to infer or predict attributes of a suitable storage array. The workload may be associated with a scaling factor that influences the determination of a suitable storage array.

Abstract: Systems and methods are provided for providing recommended resolutions to issues occurring during operation of an Information Handling System (IHS). Similar issues, each associated with resolution information specifying one or more recommended resolutions, are identified within an issue repository. Collaborative filtering is utilized on the similar issues and associated resolution information to generate a first ranked list. Collaborative filtering is also utilized on a subset of the similar issues and associated resolution information to generate a second ranked list of the subset of similar issues and associated resolution information, where the subset of similar issues are each selected based on being within a time proximity to the reported issue. A linear regression is utilized on the first ranked list and the second ranked list to generate a ranked list of recommended resolutions to the reported issue that is then provided to an administrator.

Abstract: Methods, apparatus, and processor-readable storage media for automatically adjusting storage system configurations in a storage-as-a-service environment using machine learning techniques are provided herein. An example computer-implemented method includes obtaining performance-related data for at least one storage system in a storage-as-a-service environment; processing at least a portion of the obtained performance-related data using one or more rule-based analyses; identifying, based at least in part on results of the processing, one or more storage system configurations, of the at least one storage system, requiring adjustment; determining, using at least one machine learning technique, one or more adjustment amounts for the one or more storage system configurations; and automatically adjusting the one or more storage system configurations, within the storage-as-a-service environment, in accordance with the one or more determined adjustment amounts.