Abstract: 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.

Abstract: 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.

Abstract: Methods, apparatus, and processor-readable storage media for automated predictive infrastructure scaling are provided herein.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In some examples, a computing device may gather data associated with activities performed by individuals from multiple locations (e.g., code repositories). The computing device may determine data gathered by a data monitor application at individual locations of the multiple locations over a predetermined amount of time. The computing device may filter, based on criteria, the gathered data and perform an analysis of the filtered data using a machine learning algorithm (e.g., an artificial neural network and a logistic sigmoid). The criteria may be selected based at least in part on a job function associated with the particular individual. The machine learning algorithm may create a human resource evaluation of a particular individual of the plurality of individuals recommending an increase in salary, a bonus, or a promotion. The human resources evaluation may include a probability that the particular individual will leave a current job in the organization.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In some examples, while a user is navigating a site using a browser, a computing device may receive one or more images from a camera and may monitor input data from input devices (e.g., mouse, keyboard). After a particular event occurs (e.g., a navigation event such as selecting a link, selecting a tab, scrolling up or down, or the like), the computing device may analyze the images captured after the event using a machine learning algorithm to determine a micro-expression of the user. The micro-expression may be classified as a particular sentiment of a plurality of sentiments, associated with the event, and sent to a server. The server or the computing device may instruct the browser to modify, based on the sentiment, a portion of the site. The modification may include displaying a user interface to enable the user to communicate with a representative associated with the site.

Abstract: A method, system and computer-usable medium are disclosed for providing personalized marketing at a mobile device when in proximity to a retail location. One general aspect includes a computer-implemented method for personalized proximity marketing including: detecting whether a mobile device is proximate a retail location of a product retailer; if the mobile device is proximate a retail location of the product retailer, querying a user profile associated with a user of the mobile device to determine whether the retail location has one or more items of interest available to the user of the mobile device, where the one or more items of interest are identified based on product preferences associated with the user profile; and in response to the query, alerting a user at the mobile device one or more of the items of interest are available at the retail location.

Abstract: 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.

Abstract: 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.

Abstract: In some examples, a search module executing on a computing device may determine that text input has been entered into a search entry field of a search engine. The module may retrieve audio data stored in a buffer. The audio data may be retrieved using an application programming interface of an operating system. The audio data may include audio captured by a microphone prior to the text input being entered. The module may send a search request that includes the text input and context data derived from the audio data to a search engine. The context data may comprise the audio data or additional text derived from the audio data. The context data may be included in metadata of the search request. The search engine may perform a search based on the text input and the context data and provide search results that are displayed in the browser.