Dell Products Inc.

Emc IP Holding Company LLC

Dell & Products L.p.

Parminder Singh Sethi

Lakshmi Saroja Nalam

Shelesh Chopra

Malathi Ramakrishnan

Anay Kishore

Deeder M Aurongzeb

Durai S Singh

Suren Kumar

Sathish Kumar Bikumala

Madhuri Dwarakanath

Hung The Dinh

Akanksha Goel

Atishay Jain

Bing Liu

Kanika Kapish

Anannya Roy Chowdhury

Bijan Kumar Mohanty

Praveen Kumar

Nithish Kote

Nithish Kn

Shree Ramakrishna Rathinasamy

Parmeshwr Prasad

Vaideeswaran Ganesan

Malathi R

Anshul Saxena

Vasanth Ds

Vasanth Sathyanarayanan

Shibi Panikkar

Rahul Deo Vishwakarma

Sabu K Syed

Mohammad Rafey

Kannappan Ramu

Veena Ramarao

Avinash Vishwanath

Thanuja C

Deepak NagarajeGowda

Sharmad Naik

Chandroma Ghose

Prabhat Chandra Biswas

Arjit Rastogi

Sanjay Singh

Aarsi Kumar

Srinath Kappgal

Jace William Files

Daniel Lawrence Hamlin

Gokul Thiruchengode Vajravel

Ravikanth Chaganti

Maxim Balin

Chitrak Gupta

Mohamed Sohail

Deepak Gowda

Vinod Durairaj

James S Watt, Jr

Natasha Gaurav

Noga Gershon

Sean Creedon

Landon Martin Chambers

Pandiyarajan Mani

Bhuvaneswari Tamilchelvan

Akshita Das

Vamsi Krishna Tadikamalla

Veena R

Lakshmi Nalam

Vinay Kumar Lokeshappa Malligere

Philip Sheaff

Vasanth D Sathyanaraya

Priyansh Saxena

Lakshmi Soroja Nalam

Kunal Visoulia

Karan Arora

Elanjelian V

Smita Kapil

Vivekananda Hegde

Mohith Shanthigodu

Lakshmi Narashimhan Mc

Abhishek Santosh

C Narasimhaiah

Avinash V Patil

Liane Ghosh

Tamilchelvan Bhuvaneswari

Lakshmi Narasimhan Mc

Selvaraja Subburaja

Techniques determining a data migration strategy in an edge network including a plurality of edge nodes is described. One example method includes determining a configuration for each of the plurality of edge nodes in the edge network; predicting a future workload associated with each of the plurality of edge nodes in the edge network based on an observed past workload for each of the plurality of edge nodes; receiving a migration request for a particular edge node in the plurality of edge nodes; and identifying a target edge node in the plurality of edge nodes that is suitable for handling the predicted future workload of the particular edge node, wherein the identifying is based at least in part on the determined configuration of the particular edge node and the target edge node, and on the predicted future workload of the particular edge node and the target edge node.

Determining data migration strategy in heterogeneous edge networks

Automated order management techniques for order problem learning and mitigation in an order processing system are disclosed. For example, a method comprises obtaining historical order data associated with an order processing system. The method applies a reinforcement learning-based algorithm to the obtained historical order data to generate a state-based data structure that reflects successes and failures of actions associated with previous orders processed by the order processing system. The state-based data structure is utilized to identify one or more issues in a given pending order, and recommend one or more mitigating actions to remedy the one or more issues in the given pending order.

Intelligent order problem learning and mitigation in automated order processing system

A method for managing policies for a disaster recovery of a data center includes receiving, at a cloud module, a first set of data that includes data center infrastructure information associated with the data center, a first prediction of resources, a list of assigned disaster recovery resources, and a status report of an application using the assigned disaster recovery resources; generating, by the cloud module and using the first set of data as training data, a learning model; generating a second prediction of resources using the learning model; and assigning, using the cloud module, disaster recovery resources to obtain assigned disaster recovery resources based on the second prediction, and the cloud module activates the assigned disaster recovery resources in response to determining that the data center has experienced a failure.

Method and system for disaster recovery policy management and updating

A method for enhancing data security includes receiving data. The method includes obtaining a plurality of contextual factors associated with the data. The further method includes determining an encryption level based on at least one of the contextual factors. The method further includes determining a type of encryption based on the encryption level. The method further includes encrypting the data using the type of encryption to generate encrypted data. The method further includes associating an access control level with the encrypted data to generate associated encrypted data. The method also includes determining a storage location for the associated encrypted data based on the encryption level, wherein determining the storage location is based on a data sensitivity associated with the data or a compliance rule. The method further includes sending the associated encrypted data to the storage location.

Contextual encryption and access control of data

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 the health of a device used by the applications and prioritizing the migration of applications based, in part, on the health of the devices executing the applications that are to be migrated.

Device health driven migration of applications and its dependencies

A videoconferencing application 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 power analytics, software application analytics, a geographic location, and a determined CO2 emissions value, a processor to determine a videoconferencing CO2 emissions value that exceeds a threshold value, based on the determined CO2 emissions value and power consumed during execution of a videoconferencing application at the client information handling system, the processor to identify a currently active, user-adjustable videoconferencing policy setting for the videoconferencing application, and the network interface device to transmit a recommended CO2 reduction instruction for display at the client information handling system to de-activate the user-adjustable videoconferencing policy setting to limit the videoconferencing CO2 emissions value to the CO2 emissions threshold value.

System and method for minimizing greenhouse gas emissions due to execution of videoconferencing software applications on client information handling systems across an enterprise

A carbon dioxide (CO2) emissions state transition prediction system executing on a unified endpoint management platform information handling system may comprise a network interface device to receive operational telemetry measurements for a first client device during routine monitoring intervals, including a determined CO2 emissions value, and a non-eco-friendly state transition threshold determined for the first client device, a hardware processor to predict, via a neural network modeling relationships between changes in CO2 emissions values over a most recent monitoring period and changes in user-adjustable operational telemetry measurements over a prior monitoring period, that a future determined CO2 emissions value for the first client information handling system determined based on operational telemetry measurements recorded during the current routine monitoring interval will exceed the non-eco-friendly state transition threshold value, and the network interface device to transmit an indication for user display that the first client device is predicted to reach a non-eco-friendly state.

System and method for predicting increase in greenhouse gas emissions during operation of client information handling system over device life cycle

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

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Bengaluru, India

Parminder Singh Sethi