Dell Products Inc.

Emc IP Holding Company LLC

Parminder Singh Sethi

Lakshmi Saroja Nalam

Shelesh Chopra

Anay Kishore

Suren Kumar

Durai S Singh

Deeder M Aurongzeb

Malathi Ramakrishnan

Sathish Kumar Bikumala

Hung The Dinh

Madhuri Dwarakanath

Akanksha Goel

Bing Liu

Kanika Kapish

Atishay Jain

Anannya Roy Chowdhury

Bijan Kumar Mohanty

Nithish Kote

Shree Ramakrishna Rathinasamy

Parmeshwr Prasad

Vaideeswaran Ganesan

Malathi R

Anshul Saxena

Vasanth Ds

Vasanth Sathyanarayanan

Nithish Kn

Praveen Kumar

Shibi Panikkar

Rahul Deo Vishwakarma

Sabu K Syed

Mohammad Rafey

Kannappan Ramu

Thanuja C

Veena Ramarao

Avinash Vishwanath

Deepak NagarajeGowda

Sharmad Naik

Prabhat Chandra Biswas

Chandroma Ghose

Aarsi Kumar

Sanjay Singh

Arjit Rastogi

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

Akshita Das

Vamsi Krishna Tadikamalla

Bhuvaneswari Tamilchelvan

Veena R

Pandiyarajan Mani

Philip Sheaff

Vinay Kumar Lokeshappa Malligere

Srinath Kappgal

Priyansh Saxena

Vasanth D Sathyanaraya

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 method and system for machine learning based predictions of upgrade testing outcomes for information technology (IT) environments. At least with respect to computing, an upgrade may entail the replacement of a product—in the form of hardware, software, and/or firmware—with a newer or better version, which may serve to bring the product up to date, improve the characteristic(s) of the product, and/or resolve any issue(s) inflicting the product. Further, through the process of upgrade testing, an upgrade may be assessed to determine its impact on an operability of varying environments following application of the upgrade thereon. Moreover, each environment may reflect a different history of product upgrades that had been applied thereon prior to the upgrade being tested. The disclosed method and system, accordingly, propose a framework directed to ascertaining the post-upgrade outcomes of actual IT environments using machine learning applied to post-upgrade outcomes of simulated IT environments.

Machine learning based predictions of upgrade testing outcomes for information technology environments

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 determining periods of time when the application has decreased use or idle time. This decreased use or idle time is used to determine the optimal time to perform a migration of the application. Based on the calculated migration time as well as the time it takes to load the application after migration, the maintenance planner can alert users and help insure a successful migration.

Application usage and auto maintenance driven migration of applications and their dependencies

Intelligent workload management techniques in a heterogenous computing environment are disclosed. For example, a method comprises obtaining first identifying information associated with a set of workload types, and obtaining second identifying information associated with a set of server configurations associated with a computing environment, wherein one or more server configurations in the set of server configurations are different than one or more other server configurations in the set of server configurations. The method then maps individual ones of the set of workload types to individual appropriate ones of the set of server configurations based on the obtained first and second identifying information, and causes distribution of one or more workloads to one or more servers in the computing environment, in accordance with the mapping, for execution thereon. At least a portion of the obtaining and mapping steps are performed using one or more machine learning algorithms.

Intelligent management of workloads in heterogeneous computing environment

An Internet of Things (IoT) edge gateway device information handling system executing an IoT edge gateway device location optimization and carbon dioxide (CO2) minimization system may comprise a network interface device receiving, at an edge gateway initial location, power consumption analytics and locations of AP IoT servers transceiving IoT data with the IoT edge gateway in an enterprise wireless area network (WAN), a processor executing code instructions to determine an optimal location for the IoT edge gateway that reduces total estimated CO2 emissions during IoT data transmission between the AP IoT servers and the IoT edge gateway, based on the power consumption analytics and current locations of the AP IoT servers to a new optimal location in the enterprise WAN, and the network interface device transmitting an instruction to physically move the IoT edge gateway to the optimal geographic location to minimize CO2 emitted during transceiving of the IoT data.

System and method for minimizing CO2 emissions with geographic placement of internet of things (IoT) device with respect to data geolocations for IoT data gathering

A carbon dioxide (CO2) emission tracking system operating on a client information handling system may include a processor, a network interface device, and a display to display 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, the processor to receive from a Unified Endpoint Management (UEM) platform a distributed power CO2 emissions value estimating CO2 emission per Watt of power consumed at the location, determine a power CO2 emissions value estimating CO2 emitted during a measured consumption of power, based on the hardware power consumption analytics, determine an application CO2 emissions value estimating CO2 emitted by the software application during consumption of hardware resources contributing to the power CO2 emissions value, and display the power CO2 emissions value to provide a carbon footprint for computing activities of the client information handling system.

System and method for tracking greenhouse gas emissions associated with application and power usage at an information handling system

A method comprises receiving data corresponding to operation of one or more edge devices, and analyzing the data using one or more machine learning algorithms to determine whether the operation of the one or more edge devices is anomalous. In determining whether the operation of the one or more edge devices is anomalous, the one or more machine learning algorithms determine whether the operation of the one or more edge devices deviates from identified normal operational parameters of the one or more edge devices. At least one action to control access to the one or more edge devices is triggered in response to determining that the operation of the one or more edge devices is anomalous.

Dynamic behavior-based adaptive access control system for edge devices

A method for monitoring requests in data clusters includes: obtaining, by a security management module (LMM) of a data cluster, a session registration request from a client; in response to obtaining the session registration request: generating a token using token generation parameters; generating session information and storing the session information in a database; sending session registration acknowledgement to the client, wherein the session registration acknowledgement comprises the token; and validating requests obtained from the client using the token.

Method and system for performing request validation for a data cluster

A method for managing data migration includes: receiving, by a target storage device, a migration request for migration of application data from a source storage device to the target storage device, in response to the migration request: sending a security remote security script to the source storage device, obtaining a response based on execution of the security remote security script on the source storage device, performing a security classification on the source storage device based on the response to obtain a security tag for the source storage device, determining a migration strategy for the migration of the application data from the source storage device based on the security tag, and implementing the migration strategy to migrate the application data from the source storage device to the target storage device.

System and method for determining the security status of a source device based on the security requirement of the destination device

A method for managing data replication includes: sending, by a source storage device and based on a predictive failure, a migration request to a target storage device for performing a migration of application data from the source storage device to the target storage device, in response to the migration request: obtaining a security remote script to the source storage device, implementing the security remote script on the source storage device to generate a security report, sending a response to the security remote script to the source storage device, wherein the response comprises the security report, obtaining a migration initiation request from the source storage device, wherein the migration initiation request comprises a migration strategy, and implementing the migration strategy to perform the data migration.

System and method for managing a data migration based on a security status of the source storage device

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

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Bengaluru, India

Parminder Singh Sethi