Dell Products Inc.

Emc IP Holding Company LLC

Lakshmi Saroja Nalam

Parminder Singh Sethi

Shelesh Chopra

Durai S Singh

Malathi R

Vasanth Ds

Vasanth Sathyanarayanan

Sathish Kumar Bikumala

Madhuri Dwarakanath

Atishay Jain

Bing Liu

Sean Creedon

Avinash Vishwanath

Matthew Clayton Harris

Parmnder Singh Sethi

Apoorva Sharma

A method comprises receiving data corresponding to operation of a plurality of edge devices from respective ones of a plurality of edge computing sites. The data comprises requests received by the edge devices to perform a plurality of tasks. The data is analyzed using a first machine learning algorithm to determine workloads of respective ones of the edge devices. The method further comprises predicting future workloads of the edge devices. The predicting is performed using a second machine learning algorithm and is based on the determined workloads of the edge devices. A determination is made whether to transfer at least a portion of one or more of the tasks from a first edge device to a second edge device. The first and second edge devices are located at first and second edge computing sites, respectively, and the determination is based on one or more of the predicted future workloads.

Workload management between edge computing sites

Embodiments described herein relate to methods, systems, and non-transitory computer readable mediums storing instructions for creating and executing migration workflows to replicate or migrate data (collectively referred to herein as 'migration') from one device or set of devices to another device or set of devices (e.g., from one computing environment to another). The method of migration involves using a slicing customizer to assign the applications, or parts of the applications, to network slice queues based on predetermined rules. The slices in the queues are then migrated by a network slicing controller.

Context driven network slicing based migration of applications and their dependencies

A method for performing data migration for devices includes identifying, by a device migration delegator of a device, a migration delegation event associated with the device; in response to identifying the migration delegation event: selecting at least one delegation action based on the migration delegation event; requesting a duplicator to allocate another device for pairing; obtaining a paired device from the duplicator; obtaining a token from the paired device; authenticating the paired device using the token; authorizing the paired device to perform the at least one delegation action using the token; performing at least one non-delegation action of migration by the device; and initiating, by the device, the performance of the at least one delegation action by the paired device.

Method and system for pairing computing devices for offloading actions during data migration from degrading computing devices

An apparatus comprises a processing device configured to receive system state information corresponding to one or more devices, to predict a usage frequency of the system state information using one or more machine learning models, and to determine, based at least in part on the usage frequency, a compression level for storage of the system state information. The compression level is applied to the system state information to generate at least one compressed file for transmission to a database.

Predicting usage of system state information to determine compression levels

A method comprises generating a plurality of device trees for respective ones of a plurality of devices. The plurality of device trees respectively comprise a plurality of nodes corresponding to a device, one or more components of the device and one or more alerts associated with the one or more components. In the method, weights are assigned to the plurality of nodes of the plurality of device trees, and rates of degradation for the respective ones of the plurality of devices are computed based, at least in part, on the assigned weights. Workloads for the respective ones of the plurality of devices are predicted using one or more machine learning models, and a ranking of the respective ones of the plurality of devices is generated based, at least in part, on at least one of the rates of degradation and the predicted workloads.

Device management based on degradation and workload

A method for managing edge devices includes: monitoring network traffic between an edge device (ED), an edge node (EN), and a cloud device (CD) to obtain a first dataset; monitoring an activity performed on the ED to obtain a second dataset; analyzing the first dataset to generate a network traffic analysis output (NTAO); analyzing the second dataset to generate an activities analysis output (AAO); performing a first tagging of the ED under as either a secured devices category or an unsecured devices category based on the NTAO, AAO, and a first identifier; performing a second tagging of the ED under a corresponding unsecured devices sub-category; upon tagging the ED and based on the AAO, making a determination that the activity is a malicious activity; and implementing a security policy to disrupt a user of the ED.

Method and system for smart recommendation and dynamic grouping of devices for a better device management

In accordance with one or more embodiments of the invention, a method is provided for configuring resources in a cloud environment. A request is received from a user requesting that a migration of one or more services to a cloud environment begin. Once the request is received, the method determines a default resource provisioning of the cloud environment. The one or more services are analyzed to determine their current resource usage and based on their current usage, the method forecasts future resourced requirements of the one or more services. The method then compares the forecasted future resource requirements with that of the default resource provisioning of the cloud environment, and when the comparison is greater than a predetermined threshold a new level of resource provisioning is recommended.

Method to auto correct the default resource allocation of services in a migration environment

A method for upgrading a client device includes: performing an emulation to generate an application upgrade strategy to apply a plurality of application upgrades on a client device, in which the application upgrade strategy is associated with a plurality of emulated boot times; comparing the plurality of emulated boot times with an actual boot time obtained from the client device; determining, based on the comparison, a boot time delay; generating a final application upgrade strategy, in which the final application upgrade strategy specifies the boot time delay; and initiating upgrading of the client device using the final application upgrade strategy in response to feedback from a user.

Method and system for determining a boot time delay for an upgrade of a computing device

Embodiments described herein relate to methods, systems, and non-transitory computer readable mediums storing instructions for creating and executing migration workflows to replicate or migrate data (collectively referred to herein as 'migration') from one device or set of devices to another device or set of devices (e.g., from one computing environment to another). The method of migration involves obtaining relationship information with regards to the applications that will be migrated from one device to another device or set of devices, and classifying the applications based on their relationships such as HAS-A and IS-A. Based on these classifications, one or more embodiments of the invention generate a relationship matrix in order to assign priority to the migration of each individual application. The applications are then migrated based on the matrix and assigned priorities.

Context-driven framework to migrate the applications and its dependencies by analyzing the relationships

A method for performing power management services for devices includes identifying, by a power manager, a power schedule generation event associated with devices; in response to identifying the power schedule generation event: identifying relationships between containers of the devices; generating a relationship matrix based on the identifying; obtaining network information associated with the containers; generating a prediction model using the relationship matrix and the network information; generating power schedules associated with the devices using the prediction model; and performing power management services for the devices based on the power schedule.

Method and system for managing power consumption for devices

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.

System and method to identify and improve connection issues

In one aspect, an example methodology implementing the disclosed techniques includes, by a disk controller of a plurality of disks of a data storage system, collecting usage telemetry from the plurality of disks and initiating determination of one or more candidate time slots for performing a battery learn cycle using a machine learning model. The machine learning model is trained using the usage telemetry from the plurality of disks. The method also includes, by the disk controller, presenting one or more of the one or more candidate time slots for performing a battery learn cycle.

Battery learn cycle management

A method for managing data migration includes: obtaining a confidence level of a predicted failure state of an internal component (IC) of a source device, in which the IC is one of a plurality of ICs in the source device; making a first determination that the confidence level exceeds a predetermined confidence level threshold; making, based on the first determination, a second determination that a high priority network slice of a network exists; mapping, based on the second determination, data stored in the IC to the high priority network slice; and migrating the data to a target device via the high priority network slice.

Method and system for health driven network slicing based data migration

Embodiments described herein relate to methods, systems, and non-transitory computer readable mediums storing instructions for performing a connection test prior to bringing an application on-line. The method determines a best time to perform a connection test on an application that is currently off-line and at that time performs the connection test. The connection test is performed between the first application and those applications and components to which it is related. The connection test establishes whether prior to bringing the application on-line, for example after a migration, the connections between it and all the applications and components that are related to it have been re-established with performance within a predetermined threshold.

Predictive load driven proactive pre-flight check for applications

A method for managing application migration includes: inferring dependencies and connectivity among applications executing on a system; generating a target infrastructure node (IN) configuration template based on the inferred dependencies and connectivity; making a determination that a configuration of a target IN does not satisfy the template, in which an application is executing on an IN, in which the application is not executing on the target IN; in response to the determination: obtaining a weight for each inferred parameter in the template, in which the inferred parameters comprises the inferred dependencies and connectivity, in which the weight for each inferred parameter is configured by a user; executing, based on the weights, a model to obtain a deviation score for a second target IN; generating, based on the deviation score, a migration recommendation specifying the second target IN; and initiating a display of the migration recommendation to the user.

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Bengaluru, India

Lakshmi Saroja Nalam