Dell Products Inc.

Emc IP Holding Company LLC

Parminder Singh Sethi

Lakshmi Saroja Nalam

Shelesh Chopra

Anay Kishore

Suren Kumar

Durai S Singh

Sathish Kumar Bikumala

Malathi Ramakrishnan

Deeder M Aurongzeb

Hung The Dinh

Madhuri Dwarakanath

Akanksha Goel

Bing Liu

Kanika Kapish

Anannya Roy Chowdhury

Bijan Kumar Mohanty

Atishay Jain

Parmeshwr Prasad

Vaideeswaran Ganesan

Malathi R

Anshul Saxena

Vasanth Ds

Nithish Kn

Nithish Kote

Vasanth Sathyanarayanan

Shree Ramakrishna Rathinasamy

Shibi Panikkar

Rahul Deo Vishwakarma

Sabu K Syed

Mohammad Rafey

Kannappan Ramu

Thanuja C

Avinash Vishwanath

Veena Ramarao

Praveen Kumar

Deepak NagarajeGowda

Sharmad Naik

Chandroma Ghose

Prabhat Chandra Biswas

Sanjay Singh

Arjit Rastogi

Aarsi Kumar

Jace William Files

Daniel Lawrence Hamlin

Gokul Thiruchengode Vajravel

Ravikanth Chaganti

Chitrak Gupta

Maxim Balin

Deepak Gowda

Mohamed Sohail

Vinod Durairaj

James S Watt, Jr

Natasha Gaurav

Noga Gershon

Sean Creedon

Landon Martin Chambers

Veena R

Bhuvaneswari Tamilchelvan

Akshita Das

Vamsi Krishna Tadikamalla

Vinay Kumar Lokeshappa Malligere

Priyansh Saxena

Philip Sheaff

Vasanth D Sathyanaraya

Pandiyarajan Mani

Srinath Kappgal

Lakshmi Soroja Nalam

Karan Arora

Elanjelian V

Smita Kapil

Vivekananda Hegde

Mohith Shanthigodu

Lakshmi Narashimhan Mc

C Narasimhaiah

Avinash V Patil

Liane Ghosh

Tamilchelvan Bhuvaneswari

Lakshmi Narasimhan Mc

Selvaraja Subburaja

Abhishek Santosh

Kunal Visoulia

A hardware right to repair prioritization system comprising a graphical user interface (GUI) to receive partial rankings of requirements for prioritizing an failing component replacement, where each requirement describes a characteristic having varied values across the failing components, a processor to generate a priority requirement ranking for all of the requirements, based on the partial rankings, determine a replacement priority rank for each failing component, based on characteristic values for each failing component and priority requirement ranking; determine a change in carbon dioxide (CO2) footprint resulting from replacement of each of the failing components, generate a CO2 optimized replacement priority rank by weighting the replacement priority rank by the change in CO2 footprint for each of the failing components; identify a failing component associated with a highest CO2 optimized replacement priority rank as a top replacement candidate, and the GUI to recommend immediate replacement of the top replacement candidate.

Carbon footprint optimized system and method for recommending a priority for replacement or workload redistribution for hardware across an enterprise system

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.

Management of device based on CPU temperature during hibernation state

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

Methods, apparatus, and processor-readable storage media for determining input sentiment by processing text data and non-text data using artificial intelligence techniques are provided herein. An example computer-implemented method includes dividing user input data into at least a first set of text data and a set of non-text data; converting at least a first portion of the non-text data into at least a second set of text data; classifying at least a portion of the at least a first set of text data and at least a portion of the at least a second set of text data in accordance with sentiment-related categories using a first set of artificial intelligence techniques; classifying at least a second portion of the non-text data in accordance with the sentiment-related categories using a second set of artificial intelligence techniques; and performing automated actions based on the classifying of the text data and/or the non-text data.

Determining input sentiment by automatically processing text data and non-text data using artificial intelligence techniques

A method for performing an application migration operation, the method includes initiating the application migration operation to migrate an application from a source device to a target device, where the source device includes a source data migration agent and the target device includes a target data migration agent. The method also includes initiating an active scan. Further, the method includes establishing a secure data path between the source device and the target device. Moreover, the method includes making a first determination, using the active scan, that the network traffic or the source performance metrics are indicative of suspicious activity and logging information about the suspicious activity. The method also includes making a second determination that the application migration operation is complete and terminating the active scan and the secure data path based on the second determination.

Application migration vulnerability and device performance monitoring

A system for remediating a recovery process such as a migration of data includes an event database, a monitoring component, programmed to: monitor a source device to detect an initiation of a recovery process, wherein the recovery process is migration of application data from the source device to a target device, an analysis component programmed to: monitor the event database to detect a new event entry, wherein the event entry is associated with the recovery process of the source device, make a determination that the new event entry is associated with a failure event, based on the determination, provide the new event entry to a remediating component, programmed to obtain a recommendation for remediation based on the failure event and initiate the remediation based on the recommendation for remediation.

Method and system for managing failure events of a recovery process using a recovery module

Methods, apparatus, and processor-readable storage media for predicting component lifespan information by processing user install base data and environment-related data using machine learning techniques are provided herein. An example computer-implemented method includes obtaining install base data associated with at least one system component and environment-related data associated with usage of the at least one system component; performing feature analysis on at least a portion of the obtained data using a first set of machine learning techniques; clustering, based on the feature analysis, at least a portion of the install base data and at least a portion of the environment-related data into one or more groups using a second set of machine learning techniques; generating at least one lifespan information prediction attributed to the at least one system component based on the clustering; and performing at least one automated action based on the at least one lifespan information prediction.

Predicting component lifespan information by processing user install base data and environment-related data using machine learning techniques

A noise and carbon dioxide (CO2) emissions minimizing load-balancing system executing on a unified endpoint management platform information handling system comprising a network interface device to receive telemetry measurements for a first server operating within peak hours, including power analytics, geographic location, ambient temperature and humidity, CO2 emissions value, and measured noise level exceeding a first CO2 load-balancing noise threshold value for the first server, a hardware processor to reject processing requests made to the first server above a CO2 optimal request load predicted, via a neural network modeling a relationship between changes in CO2 emissions values and changes in the telemetry measurements to cause the first server to emit noise below the first CO2 load-balancing noise threshold value, and the hardware processor to redistribute rejected processing requests across candidate offload servers emitting noise below a candidate-specific CO2 load-balancing noise threshold value determined for each of the candidate offload servers.

System and method for load-balancing processing requests across client information handling systems to minimize noise and carbon dioxide emissions in a data center

A hardware life extension learning recommendation and reward system of an information handling system may comprise a network interface device to receive operational telemetries from a first and a second computing device that each include a high temperature warning, an error for a labeled hardware type, and execution of a specific application, and a processor to determine, based on these telemetries, that failure of a first component of the labeled hardware type previously occurred at the first device, failure of a second component of the labeled hardware type has occurred at the second device, and execution at the first device of a previous recommendation to limit access to resources of the first component made available to the specific application extended the first component lifetime. A new recommendation may be transmitted to the second device to limit access to resources of the second component to the specific application.

System and method for recommending and rewarding configuration adjustments for extending information handling system hardware life

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 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.

Data management operations for data from edge locations

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

In general, embodiments relate to a method for managing a technical support (TS) session on a technical support system. The embodiments include receiving TS correspondence from a client, wherein the TS correspondence is associated with the TS session, classifying the TS correspondence to assign it a question classification, based on the question classification, making a determination that the TS correspondence matches at least one prior received TS correspondence, wherein the at least one prior received TS correspondence is associated with the TS session, and visually identifying the TS correspondence and the at least one prior received TS correspondence on a graphical customer interface (GUI) of the technical support system.

Method and system to manage tech support interactions using dynamic notification platform

An adaptive carbon dioxide (CO2) emissions reduction system operating on a client information handling system may comprise a processor, a network interface device, and a graphical user interface (GUI) to gather analytics for an executing software application hardware power consumption analytics, and a location for the client information handling system, determine a power CO2 emissions value estimating CO2 emitted during a measured consumption of power at the location based on the hardware power consumption analytics, determine that updated power consumption exceeds a maximum power draw threshold value determined based on user input for decreasing the power CO2 emissions value and location, and display a hardware reconfiguration recommendation for reconfiguring a hardware component currently configured for high performance to a power conservation setting.

System and method for adaptively reducing carbon footprint for an information handling system

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Bengaluru, India

Parminder Singh Sethi