Abstract: A method comprises receiving data from a plurality of devices and analyzing the data to identify one or more parameters for segregation of the data. In the method, the data is tagged with one or more metadata tags corresponding to the one or more parameters. The method further comprises segregating the data into a plurality of data portions based at least in part on the one or more parameters, and storing respective ones of the plurality of data portions in respective data stores of a plurality of data stores. The one or more parameters comprise at least one of one or more policies corresponding to the data and respective locations of the plurality of devices.

Abstract: A method comprises analyzing operational data corresponding to at least one processing unit of a device, and deriving a threshold processing unit temperature for initiation of a hibernation state of the device based at least in part on the analyzing. In the method, the temperature of the at least one processing unit is monitored, and a determination is made whether the temperature of the at least one processing unit has reached the threshold processing unit temperature. The hibernation state of the device is initiated responsive to determining that the temperature of the at least one processing unit has reached the threshold processing unit temperature. A command to initiate the hibernation state is transmitted over an out-of-band communication channel.

Abstract: A method for managing restoration includes receiving, by a restoration orchestrator, a restoration request for the restoration by a restoring device, in response to the restoration request: identifying a downtime preparation time for the restoration, collecting, during the downtime preparation time, service requests from the restoring device, forwarding the service requests to a digital twin device, after the forwarding, making a determination that the downtime preparation time is complete, and based on the determination, initiating a re-servicing of the restoring device, wherein the restoration is complete after the determination.

Abstract: A method for providing enhanced reliability for data streams includes identifying an upgrade request sent by a client device to an upgrade node. The method also includes monitoring a response to the upgrade request from the upgrade node to the client device. The method further includes determining that the response is indicative of a connection issue between the client device and the upgrade node. Moreover, the method includes identifying, based on determining that the response is indicative of the connection issue, a support agent associated with the client device. In addition, the method includes providing an alert to the support agent with an indication that there is the connection issue between the client device and the upgrade node.

Abstract: A method for managing a data center includes: receiving metadata associated with a data center component (DCC); making a first determination that the DCC is valid; analyzing, based on the first determination, the metadata to extract relevant data; obtaining a current state of the DCC based on the relevant data; inferring a future state of the DCC based on the current state of the DCC; obtaining a confidence score of the future state of the DCC based on the relevant data; making a second determination that the confidence score exceeds a predetermined maximum confidence score; generating, based on the second determination, a failure report associated with the DCC, wherein the report comprises at least the current state of the DCC and future state of the DCC; and providing the failure report to a vendor of the DCC to notify an administrator of the vendor.

Abstract: In general, embodiments described herein relate to methods, systems, and non-transitory computer readable mediums storing instructions for re-configuring and/or repairing at least one datacenter element, when a change is detected in the datacenter. In one or more embodiments the datacenter may be running multiple applications on multiple individual computing devices. Each of these devices need to be correctly configured both locally and in the larger computing system in order to allow the applications to function correctly and efficiently. An analyzer, along with other components of the datacenter, determines dependencies and remediation steps for elements such as hardware and/or applications of the datacenter that have been changed. Based on determined dependencies and the system configuration, the analyzer may determine any physical changes needed to be made to the datacenter as well as ideal configurations for any affected datacenter elements including both hardware and software elements.

Abstract: One or more embodiments of the invention relate to methods, systems, and non-transitory computer readable mediums storing instructions for determining how often to create restore points. The embodiments of the invention use machine learning to evaluate the current configuration and status of a computing device to determine an optimal frequency/time for creating restore points. By utilizing machine learning to analyze current system telemetry and other information such as system logs, the one or more embodiments of the invention, are able to efficiently adjust the frequency either more frequent or less frequent given both the potential for system failure and available physical storage space. This and other improvements allow the one or more embodiments of the invention to provide for better data protection with less downtime and greater reliability.

Abstract: In general, embodiments relate to a method for adjusting a datacenter when new hardware is added. The method comprises retrieving telemetry form the datacenter and using it to detect one or more new hardware elements that have been added to the datacenter. The one or more new hardware elements are identified, and it is determined that a hardware profile template form the one or more new hardware elements does not exist in a shared storage. A component tree for the identified one or more hardware elements is created and using this component tree a hardware profile template is created for each of the identified one or more hardware elements. This profile template is saved in the shared storage and is applied to configure the datacenter to use the one or more new hardware elements.

Abstract: A hardware failure prediction and avoidance system executing on a unified endpoint management platform information handling system comprising a network interface device (NID) to receive failed operational telemetries for client devices including power and software analytics, and event viewer error logs, and an error associated with a hardware type, a processor to execute a classification supervised learning algorithm on the failed operational telemetries to determine that a hardware or software configuration will likely co-occur with a future error of the hardware type, identify the hardware or software configuration as problematic, the processor to identify the problematic system configuration in a current operational telemetry by correlating the current operational telemetry with a portion of the failed operational telemetries, and the NID to transmit a recommendation to the first client device to adjust the problematic system configuration in order to avoid occurrence of the error at the first client devic

Abstract: A method comprises receiving a data packet configured in accordance with a first network protocol version, and converting a header portion of the data packet to be configured in accordance with a second network protocol version. The converting comprises identifying a source network protocol address for a source of the data packet in the header portion in accordance with the second network protocol version, and identifying a destination network protocol address for a destination of the data packet in the header portion in accordance with the second network protocol version.

Abstract: Techniques for failure prediction and remediation are disclosed. For example, a method comprises training one or more machine learning algorithms with a training dataset corresponding to a plurality of users, wherein the training dataset comprises at least one of product purchase data, product service data and product return data corresponding to the plurality of users. In the method, an input dataset corresponding to at least one user is received. The input dataset comprises at least one of product purchase data, product service data and product return data corresponding to the user. The input dataset is analyzed using the one or more machine learning algorithms. The method further comprises predicting, based at least in part on the analyzing, a likelihood of whether at least one product corresponding to the user will fail to be returned to a product providing entity when a return of the product has been requested.

Abstract: Methods, apparatus, and processor-readable storage media for automated resource prioritization using artificial intelligence techniques are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for automated power-related device usage determinations using artificial intelligence techniques are provided herein. An example computer-implemented method includes determining power-related information for one or more devices by processing utilization data associated with the device(s) using artificial intelligence techniques; determining, based at least in part on the power-related information, multiple instances of inactivity and durations thereof for the device(s); determining at least one particular duration of inactivity for the device(s) based at least in part on a number of occurrences of each of the determined durations of instances of inactivity; generating, based at least in part on the at least one particular duration of inactivity, one or more sleep mode configuration modification recommendations for the device(s); and performing one or more automated actions based on the one or more sleep mode configuration modification recommendations.

Abstract: An apparatus comprises a processing device configured to monitor a health status of storage devices that are part of a virtual disk and to identify a first subset of the storage devices that have a first health status and a second subset of the storage devices that have a second health status. The processing device is also configured, responsive to determining that there is sufficient available storage capacity on the second subset of the storage devices to copy data from used storage capacity on the first subset of the storage devices, to resize the virtual disk to a storage capacity determined as a function of storage capacities of the second subset of the storage devices allocated to the virtual disk and to copy data from the used storage capacity on the first subset of the storage devices to the available storage capacity on the second subset of the storage devices.

Abstract: A protective carbon dioxide (CO2) emissions minimization system executing on a unified endpoint management platform information handling system may comprise a network interface device to receive operational telemetry measurements for a client information handling system including a non-eco-friendly state transition threshold value, a neural network modeling a relationship between changes in CO2 emissions values and operational telemetry measurements to predict that a user-adjustable operational telemetry measurement will cause a future determined CO2 emissions value for the client information handling system exceeding the non-eco-friendly state transition threshold value, a hardware processor to identify a remediation user instruction predicted to change operation of the client information handling system, causing a change in the user-adjustable operational telemetry measurement to prevent the future determined CO2 emissions value from exceeding the non-eco-friendly state transition threshold value, and the n

Abstract: A method comprises collecting data from a computing environment comprising one or more storage devices, wherein the data comprises information identifying assignments of the one or more storage devices to one or more servers in the computing environment. In the method, the data is analyzed using one or more machine learning algorithms, wherein the analyzing comprises predicting whether a given configuration of the one or more storage devices in combination with the one or more servers is a specified configuration. The method also includes generating a storage configuration recommendation for a user based on the analysis, and transmitting the storage configuration recommendation to the user.

Abstract: A method includes identifying relationships between containers on a first device, where each container includes at least one relationship that includes a HAS-A relationship or an IS-A relationship. The method further includes receiving a request to migrate at least a portion of the containers. The method also includes identifying a second device suitable for migration based on the request. Moreover, the method includes identifying a first group of containers from the containers to migrate to the second device based on the relationships. In addition, the method includes migrating the first group of containers to the second device.

Abstract: A method includes identifying relationships between containers on a first device, where each container includes at least one relationship that includes a HAS-A relationship or an IS-A relationship. The method also includes creating, on a second device, a first set of twin containers, where each container in the first set of twin containers corresponds to a container that includes only the HAS-A relationships. In addition, the method includes creating, on the second device and in response to creating the first set of twin containers, a second set of twin containers where each container in the second set of twin containers corresponds to a container that includes at least one IS-A relationship. Moreover, the method includes determining and performing a twin container start-up sequence for the first set and the second set of twin containers.

Abstract: A method for managing a production environment includes obtaining container information associated with application containers in the production environment, in response to the obtaining: performing an attributes analysis on a set of data attributes of the container information to obtain an initial container grouping, assigning, based on the attributes analysis, a set of migration sensitivity tags to each of the application containers, generating a migration strategy based on the set of migration sensitivity tags, and initiating a migration of at least a portion of the application containers based on the migration strategy.

Abstract: A method for managing a production environment includes obtaining telemetry information for a set of logical devices in the production environment, in response to the obtaining, perform a preprocessing of the telemetry information to obtain pre-processed data, perform a condition analysis on the pre-processed data to obtain a set of condition indicators, generating a downtime prediction model using the set of condition indicators and the telemetry information, and based on the downtime prediction model, initiating generation of a replacement logical device.