Dell Products Inc.

Dell Products L.p.

James S Watt, Jr

Bijan Kumar Mohanty

Rene Herrero

Sathish Kumar Bikumala

Mark D Owens

Kareemullah Khan Fazal

Douglas Braga De Alencar

Parminder Singh Sethi

Hung The Dinh

Sabu K Syed

Kannappan Ramu

Akanksha Goel

Frank DiRosa

Bhaskar Todi

Douglas Alencar

Someshwar Mukherjee

Methods, apparatus, and processor-readable storage media for automated predictive infrastructure scaling are provided herein. An example computer-implemented method includes generating infrastructure scaling predictions by processing, using a motion-based model, historical data pertaining to number of requests for resources for a given time interval and historical data pertaining to a rate of change in the number of requests; determining a trend based on moving average values pertaining to the historical data; determining a utilization target related to the resources based on the trend; calculating a standard deviation for resource demand based on historical utilization data pertaining to the resources; separating the standard deviation into zones related to levels of utilization of the resources; identifying one of the zones for infrastructure scaling in a future time interval based on the utilization target; updating the predictions by fitting the predictions to the identified zone; and performing automated actions based on the updated predictions.

Automated predictive infrastructure scaling

Methods, apparatus, and processor-readable storage media for correlating data center resources in a multi-tenant execution environment using machine learning techniques are provided herein. An example computer-implemented method includes obtaining multiple data streams pertaining to one or more data center resources in at least one multi-tenant executing environment; correlating one or more portions of the multiple data streams by processing at least a portion of the multiple data streams using at least one multi-tenant-capable search engine; determining one or more anomalies within the multiple data streams by processing the one or more correlated portions of the multiple data streams using a machine learning-based anomaly detection engine; and performing at least one automated action based at least in part on the one or more determined anomalies.

Correlating data center resources in a multi-tenant execution environment using machine learning techniques

In some examples, a computing device may receive, from an email server, an email rule comprising conditions and actions and determine a subset of archived emails stored on the computing device that satisfy the conditions. The computing device may perform the actions to the subset of archived emails, such as placing a legal hold on the subset of archived emails, creating and sending copies of the subset of archived emails to the email server, or deleting, from the computing device, the subset of archived emails. After determining that a user is attempting to perform a prohibited action on (e.g., delete, edit, forward) an email of the subset of archived emails, the computing device may perform a protective action, such as logging the user out of an email account, preventing the user from logging in to the email account, automatically sending an email to an administrator or the like.

Automated email legal hold

Methods, apparatus, and processor-readable storage media for device component management using deep learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more enterprise devices; determining, for each of the one or more enterprise devices, values for multiple device attributes by processing the obtained telemetry data; generating, for each of the one or more enterprise devices, at least one prediction related to lifecycle information of at least one device component by processing the determined attribute values using one or more deep learning techniques; and performing one or more automated actions based at least in part on the at least one generated prediction.

Device component management using deep learning techniques

A system, method, and computer-readable medium are disclosed for improved audio collection of website ambient data. In various embodiments, when a user visits a website, audio data is collected during a website browsing session. The audio data is converted to text by Natural Language Processing (NLP) enabled device used for browsing. From the text, certain key words are identified that are relevant to a business. The identified key words can be analyzed per requirements of the business.

Site ambient audio collection

A system of one or more computers is configured to peer container clusters running on different container orchestration systems. One general aspect includes moving an endpoint service container between an original cluster and a target cluster of a cluster mesh. In at least one embodiment, a remote service endpoint container is instantiated at the original cluster using service registry information accessed by a mesh operator. In at least one embodiment, the service registry information includes the hostname/path information for the endpoint service container operating at the target cluster. The remote service endpoint container is configured to allow the dependent container at the original cluster to consume services available at the endpoint service container at the target cluster as though the endpoint service container is local to the dependent container.

System for peering container clusters running on different container orchestration systems

A workload optimization system includes a workload manager subsystem that provides a plurality of workloads that each generates a jobs, an agent infrastructure subsystem that includes a first container host hosting a first container having a first agent and that processes jobs generated by at least one of the plurality of workloads, and a workload resource optimization subsystem that monitors a job queue of jobs that were generated by the plurality of workloads and that are to be processed by the first container. The workload resource optimization subsystem then determines whether the job queue satisfies a container generation condition. If so, the workload resource optimization subsystem provides instructions to the first container host to generate a second container that includes a second agent. The workload resource optimization subsystem monitors container host utilization to provide instructions to activate a second container host to provide a third container having a third agent.

Workload optimization system

A system for testing changes to a website includes a hypervisor that instantiates a first virtual machine, from a first snapshot stored in a repository, as a first environment node in a test environment. The hypervisor applies scripts to configure the first virtual machine to test a first proposed webpage of the website. If the test is successful, then a second snapshot of the first virtual machine as configured is stored in the repository. The hypervisor may then instantiate a second virtual machine from the second snapshot, and configure the second virtual machine to test a second webpage of the website.

System and method for provisioning a virtual machine test environment

A method includes receiving, at a software-defined load balancer in a software-defined networking (SDN) application platform from a given one of a plurality of client devices, a request for access to a given application hosted in the SDN application platform. The method also includes obtaining, from a routing database, a set of contextual routing rules for directing requests to the given application to two or more different versions of the given application hosted in the SDN application platform. The method further includes analyzing the request to determine application context information, selecting one of the two or more different versions of the given application to serve the request utilizing the application context information and the set of contextual routing rules, and providing the given client device with access to the selected version of the given application hosted in the software-defined networking application platform in response to the request.

Contextual routing for directing requests to different versions of an application

A software package retention system that uses pre-defined rules to retain software packages based on how the packages are used, and not merely based on a package's date/time of creation (age) or type. A retention policy server in the system integrates with the build, deployments, and artifact storage systems of a software supplier/vendor to ensure appropriate retentions are met for audit and regulatory compliance and unneeded artifacts or packages are purged to save storage space and lower operational costs. The server has the capability to monitor software deployments to the customer and to developer test environments, and to make rule-based decisions on when and how to run retention policy clean-up jobs and on what packages. With increasing reliance on frequent build and release of software packages across the software industry, package management post-release using the rule-based retention policy provides an efficient and cost-effective solution for legal compliance with retention requirements.

Growing community of inventors

Austin, TX, United States of America

James S Watt, Jr