Dell Products Inc.

Emc IP Holding Company LLC

Deepak NagarajeGowda

Sathish Kumar Bikumala

Bina K Thakkar

Ashutosh P Nanekar

Parminder Singh Sethi

Shibi Panikkar

Bijan Kumar Mohanty

Vasile Radoaca

Ares Sakamoto

Marcio Fragoso Stumpf Lena

Systems, methods, and computer-readable media to identify replacement parts for an electronic asset using artificial intelligence/machine learning (AI/ML). In at least one embodiment, part data may be provided to an input of a trained AI/ML parts similarity model that has been trained to determine similarities between parts, including the parts used in a plurality of electronic assets of the organization. The trained AI/ML parts similarity model uses the part data to provide information relating to parts that are compatible with the part. In certain embodiments, the information relating to compatible parts includes an identifier for compatible parts and a similarity score indicating how similar each compatible part is to the part that is to be used to service the electronic asset.

System and method for identification of replacement parts using artificial intelligence/machine learning

A system, method, and computer-readable medium for performing a data center monitoring and management operation, The data center monitoring and management operation includes receiving data center asset data for a plurality of data center assets, the data center asset data comprising data center asset fan associated data; providing the data center asset fan associated data to a fan failure prediction model; and, training the fan failure prediction model using the data center asset fan associated data.

Data center monitoring and management operation for monitoring fan status within a data center

A system, method, and computer-readable medium for performing a data center monitoring and management operation, The data center monitoring and management operation includes receiving data center data for a data center, the data center data comprising data center memory associated data; receiving data center asset data for a plurality of data center assets, the data center asset data comprising data center asset memory associated data; providing the data center memory associated data and the data center asset memory associated data to a memory failure prediction model; and, training the memory failure prediction model using the data center memory associated data and the data center asset memory associated data.

Data center monitoring and management operation for predicting memory failures within a data center

Methods, apparatus, and processor-readable storage media for scalable capacity forecasting in storage systems using a machine learning model are provided herein. An example computer-implemented method includes obtaining historical capacity data for storage objects; partitioning the obtained data into categories, wherein at least a first category comprises data from storage objects associated with a first capacity-related condition, and at least a second category comprises data from storage objects associated with a second capacity-related condition different than the first capacity-related condition; selecting a time series sequence of a designated time interval, and labeling, accordingly, portions of the data in the first category and portions of the data in the second category; training a machine learning model using the labeled data; performing capacity forecasting by processing input capacity data for at least one of the storage objects using the machine learning model; and performing automated actions based on results of the capacity forecasting.

Scalable capacity forecasting in storage systems using a machine learning model

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

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.

Data center issue impact analysis

A denial-of-service detection system includes a denial-of-service detection subsystem coupled to a plurality of storage systems via a network. The denial-of-service detection subsystem receives current first storage system data for each of a plurality of different storage system operating metrics from a first storage system included in the plurality of storage systems. Based on a historical storage system data for each of the plurality of different storage system operating metrics that was previously received from the plurality of storage devices, the denial-of-service subsystem detects an operating anomaly in the current first storage system data for at least one of the plurality of different storage system operating metrics, identifies a time-series similarity in a subset of respective time-series of the current first storage system data for each of the plurality of different storage system operating metrics for which the operating anomaly was detected and, in response, performs a denial-of-service remediation action.

Denial-of-service detection system

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.

Systems and methods for identification of issue resolutions using collaborative filtering

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).

Automatically processing storage system data and generating visualizations representing differential data comparisons

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.

System for generating data center asset configuration recommendations

The disclosed systems and methods are directed to a computer-implemented method for use in designing a service. In at least one embodiment, a method includes designing a service with one or more generic virtual network functions (VNFs), where the generic VNFs are defined independent of vendor sourcing information. One or more trained machine learning (ML) models are used to identify VNFs available from VNF vendors that may source one or more VNFs similar to the generic VNFs. The service is implemented using VNFs provided by one or more VNF vendors, where the VNFs provided by the one or more VNF vendors have network functionality generating similar to the one or generic VNFs.

Services using AI/ML to select virtual network functions and vendors for supplying the virtual network functions

In some examples, before saving a file, a device analyzes contents (e.g., text and images) of the file to create a bag of words. The device performs Latent Dirichlet Allocation of the bag of words to determine topics included in the file and orders the topics in descending order of frequency. The device generates a particular name of the file based on the ordered topics and automatically save (or suggest saving) the file with the particular name. The device determines, based on the ordered topics, a folder name of a folder in which to store the file. If an existing folder has the folder name, then the device saves the file in the existing folder. If not, then the device creates a new folder with the folder name and saves the file in the new folder.

Automated file naming and file organization using machine learning

Methods, information handling systems and computer readable media are disclosed for determining a priority score for a pending support request document. According to one embodiment, a method includes receiving current support request information from within a pending support request document and accessing current additional information associated with the pending support request document. The method further includes associating a set of parameter values with the pending support request document, wherein the values within the set of parameter values are based on information within one or both of the current support request information or the current additional information. The method continues with determining a priority score corresponding to the set of parameter values, where determining the priority score comprises applying a machine learning model developed using previous support request information and previous additional information associated with previously-resolved support request documents, and assigning the priority score to the pending support request document.

System and method for prioritization of support requests

Architectures and techniques are described that can monitor or track change to storage system configuration. Changes to the configuration that are determined to be statistically significant in potentially affecting and/or causing performance issues of the storage system can be specifically tracked. Such can be accomplished by generating a hash data of the configuration data and sorting that hash data to data buckets based on a similarity score to other hash data of other storage systems.

Storage system configuration change tracking for root cause/troubleshooting

Techniques are provided for storage system backup and de-duplication. In an example, a file system stores a plurality of files. These files can be grouped into smaller sets through a series of iterations, where a set of files is similar to each other. Then, files of a set of files can be directly compared for duplicate information relative to each other, one copy of this duplicate information can be stored, and other copies can be discarded.

Growing community of inventors

Cary, NC, United States of America

Deepak NagarajeGowda