Dell Products Inc.

Emc IP Holding Company LLC

Bina K Thakkar

Deepak Gowda

Deepak NagarajeGowda

Ashish Arvindbhai Pancholi

David C Waser

Peter Beale

Zachary W Arnold

Aditya Krishnan

Ashutosh P Nanekar

Fatemeh Azmandian

Chao Su

Roopa A Luktuke

Francisco Jaen

Lisa O'Mahony

Wenjin Liu

Shenee Prakash Ashara

Shou-Huey Jiang

Siva Rama Krishna Kottapalli

Karthik Hubli

Niharika Karia

Frederic Meunier

A system for generating test cases with workload mixes for a set of target information handling systems 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 a set of target information handling systems. A workload mix may be determined based on workload characteristics of the set of target information handling systems. Real customer workload data including real-time or near real-time workload data may be used to check a test case for accuracy before deploying a test case to a target information handling system.

System and method for automated test case generation based on queuing curve analysis

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.

Autonomous workload homing in a multi-tenant environment

A system, method, and computer-readable medium for performing a data center management and monitoring operation. The data center management and monitoring operation includes: receiving utilization information from a plurality of data center assets within a data center; assigning a load index to each of the plurality of data center assets based upon the utilization information; training a machine learning model to generate a predicted load index for each of the plurality of data center assets; and, presenting the predicted load index for each of the plurality of data center assets to a user.

Unified performance metric for identifying data center utilization

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.

System and method for automated workload identification, workload model generation and deployment

The described technology is generally directed towards processing structured data corresponding to system information such as alerts, logs, events, health check data and the like, to identify stateful flows from the structured data. Identical flows are combined, similar flows, based on similarity scores obtained from stateful flows are combined, and incremental stateful flows are combined. Neural networks can be used to identify the similar flows and incremental flows. A distribution can be obtained based on counts of the different stateful flows that remain after combining the identical, similar and incremental stateful flows; a neural network that accounts for subtle differences can be used to provide a more accurate distribution than simple counts. Anomalous stateful flows can be identified from the distribution, with some action taken for an anomalous stateful flow, e.g., to send a notification or other output to a support engineer of the like for proactive issue detection.

Structured data flow identification for proactive issue detection

The technology described herein is directed towards determining a datacenter's power consumption of its devices at the workload level, from which an objective carbon footprint impact score can be determined. Devices can include servers, network devices such as switches, and storage devices. For a group of workloads at a location, workload power consumption values can be determined based on collected power-related workload metrics data. The power consumption values are used in determining per-workload carbon footprint values for the workloads based on the location. One or more actions can be taken to modify the respective carbon footprint values, e.g., moving a workload to a different location, changing device hardware, and so on.

Carbon footprint climate impact scores for datacenter workloads

The technology described herein is directed towards optimizing power consumption of devices, e.g., in a datacenter. A modified (two-tier) genetic algorithm performs a carbon footprint-based optimization in a first tier to determine a candidate range of coefficients for each device type, e.g., servers, switches and storage devices/systems that likely reduce carbon footprint of each device type. In a second tier of the genetic algorithm, those ranges of coefficients are used in conjunction with actual power usage-based carbon footprint scores of individual devices to find respective sets of coefficients that minimize respective objective functions for the servers, the switches and the storage devices. The sets of coefficients can be used for power capping the devices. Device performance constraint-based intelligent selection can be used in one or both tiers to speed up convergence.

Datacenter carbon footprint climate impact reduction

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.

Data center issue remediation analysis user interface

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. An example computer-implemented method includes obtaining data pertaining to multiple storage systems; segmenting, into one or more segments, the multiple storage systems based on one or more vertical sectors and one or more size parameters of an enterprise associated with each storage system; identifying, within each of the segments, each storage system running one or more vertical sector-specific applications; clustering, within each of the segments, the storage systems running one or more vertical sector-specific applications based on configuration information using at least one machine learning technique; generating one or more storage system configuration templates based on the clustering; and performing one or more automated actions based on the one or more generated storage system configuration templates.

Determining storage system configuration recommendations based on vertical sectors and size parameters using machine learning techniques

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.

Automatically adjusting storage system configurations in a storage-as-a-service environment using machine learning techniques

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.

Artificial intelligence operations assisted data center monitoring and management adoption facilitator

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.

Machine learning-based anomaly detection using time series decomposition

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.

Automatically determining storage system data breaches using machine learning techniques

The described technology is generally directed towards processing time series (e.g., device telemetry) data, including identifying missing data (gaps in the time series data), and delayed data. The time series data are converted to ternary data, e.g., zero if timely, one if delayed or two if missing, and counts are obtained for each. If the missing data and/or delayed counts are significant, e.g., exceed a threshold percentage of the total data, the time series data indicates a problem that can be narrowed down to a more specific cause. For example, the time series data can be filtered by customer products/offers and customer locations, and if a filtered dataset's ternary data are similar to the problematic data, as determined via unsupervised clustering as similarity data (occurring at a similar time), the potential problem or problems can be narrowed to a potential cause based on that filtered dataset's similarity.

Time series clustering to troubleshoot device problems based on missed and delayed data

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.

Growing community of inventors

Cary, NC, United States of America

Bina K Thakkar