Dell Products Inc.

Emc IP Holding Company LLC

Shibi Panikkar

Rohit Gosain

Pratheek Veluswamy

Murali Devendranath

Sisir Samanta

Sathish Kumar Bikumala

Dhilip S Kumar

Parminder Singh Sethi

Shanir Anshul

Ching-Yun Chao

Ajay Maikhuri

Rangan Basu

Teja Madhukar Manakame

Manav Ghosh

Dong Ji

Elie Antoun Jreij

Yun Zhang

Muzhar S Khokhar

Dominique Prunier

Deepak NagarajeGowda

Robert A Lincourt

Reddeppa Kollu

Chetan Pudiyanda Somaiah

Bridget Cate

Mahesh Kumar Kancharla

Xiang Cao

Karan Kapoor

Sudeshna Dash

Tuck Meng Chin

Andrew Nepogodin

Ravi Kumar

Jason Davidson

Ravi Prakash Kulkarni

Wallace Wei

Aashna Mohapatra

Sanjukta De

Subhashini Puram

Naga Navya Nandini Nidhi Pavuluri

Thirumaleshwara Adyanadka Shama

Anup Kumar

Kwong Lung Yong

Susana Cherian

Thiru Shama

Rajesh Kumar Reddy Vasa

Sekar Ponnayan

Ajit Kar

Ravuru Vijaya Sankar

Bajrang Harlalka

Ramakanth Kommu

Jennifer Ann Felch

Arvind Babu Matta Adiseshaiah

Bhanu Pratap Singh

Deepali S Mali

Guruprasad Rao

Thirumaleshwara Shama

Systems and methods for location-based IHS disablement in an Information Handling System (IHS) are described. In an embodiment, an IHS may include: a host processor; a security processor coupled to the host processor; and a memory coupled to the security processor, the memory having program instructions stored thereon that, upon execution, cause the security processor to: detect a location where the IHS is currently disposed; determine one or more restrictions associated with the detected location, each of the restrictions specifying a limitation to be imparted on a resource of the IHS; and communicate with the IHS to apply the determined restrictions.

Location-based IHS disablement system and method

An apparatus comprises a processing device configured to identify a source database of a first database type that is to be migrated to a target database of a second database type, to determine a set of migration factors for migrating the source database to the target database, and to select a given migration machine learning model for estimating a size required for the target database based at least in part on the first database type of the source database and the second database type of the target database. The processing device is further configured to estimate the size required for the target database utilizing the given migration machine learning model and the determined set of migration factors and to provision, for the target database, a given amount of storage resources based at least in part on the estimated size required for the target database.

Machine learning-based determination of target database size for database migration operations

Edge configuration aware license key generation is presented herein. An example method comprises, in response to downloading an intelligent activation key, instantiating an edge configuration engine comprising a listening process and a configuration process, creating and associating a license file vault instance with the edge configuration engine, initiating the listening process, and based on the listening process detecting that a change has occurred with the license file vault, initiating the configuration process, wherein the configuration process configures edge computing equipment to retrieve enabling data used to activate a product associated with the intelligent activation key and enumerated in the license file vault.

Edge configuration aware license key

Techniques for task management in a parallel processing environment are disclosed. For example, a method comprises obtaining an indication of a data load for a given task, a time range within which the given task is to be executed, and a resource utilization limit for executing the task. The method computes a number of processes to be instantiated to execute the given task for the data load within the time range and the resource utilization limit. The method then schedules the given task to be executed by the computed number of processes in a parallel processing environment.

Time-bound task management in parallel processing environment

Techniques for managing containerized workloads in a container computing environment are disclosed. For example, a method comprises the following steps. In a first mode, the method learns resources and execution times needed to process incoming workloads of a first workload type and a second workload type in a set of one or more clusters in a container-based computing environment. In a second mode, based on the learning of resources and execution times in the first mode, the method determines whether a subsequent incoming workload of the second workload type can be executed by one of the set of one or more clusters or whether an additional cluster should be created to process the subsequent incoming workload and then removed after processing the subsequent incoming workload is completed.

On-demand clusters in container computing environment

Methods, apparatus, and processor-readable storage media for processing usage data for remote infrastructure using adjustable execution models are provided herein. An example computer-implemented method includes: obtaining a data structure related to a subscription request for hardware infrastructure provided by a service provider, wherein the data structure is obtained by a processing engine deployed on the hardware infrastructure at a remote location; analyzing the data structure to build an execution model comprising function blocks for processing usage data associated with the hardware infrastructure and one or more instructions to execute the function blocks; processing the usage data for a given time period in accordance with the execution model; providing execution data associated with the plurality of function blocks; and automatically adjusting the execution model for an additional time period using information obtained from the service provider, wherein the obtained information is based at least in part on the execution data.

Adjustable execution models for processing usage data for remote infrastructure

Techniques are disclosed for data management comprising self-service data integration functionalities in information processing systems. For example, a method includes accessing, by a first processing node of an information processing system associated with a data consumer, a schema for a dataset of a data publisher associated with a second processing node of the information processing system. The method utilizes, by the first processing node, the schema to select at least a subset of the dataset. The method generates, by the first processing node, a query to obtain the selected subset of the dataset from the second processing node. The method sends, by the first processing node, the query to the second processing node and then receives, at the first processing node, the subset of the dataset from the second processing node.

Data integration in an information processing system

Methods, apparatus, and processor-readable storage media for processing transactions using nested data objects are provided herein. An example computer-implemented method includes: generating a nested data object corresponding to a file, where the nested data object includes a first layer for a first transaction related to the file; adding, for each of one or more additional transactions related to the file, a corresponding additional layer to the nested data object, where each layer of the nested data object include one or more respective hash values and one or more respective private keys; and transmitting the nested data object to a user device, where each layer of the nested object are encrypted based on a type of access specified for a user of the user device, and wherein each of the transactions is verifiable based at last in part on the one or more hash values of the corresponding layer.

Processing transactions using nested data objects

A system, method, and computer-readable medium for authentication as to resources of a subscription for a customer for particular resources that include hardware and software. A distributed ledger, such as a block chain support a main subscription channel. Factory entities of the provider of the resources write to a sub distributed ledger of the distributed ledger, where the sub distributed ledger is related to a factory subscription channel. Third party vendors of the provider of the resources write to another sub distributed ledger of the distributed ledger, where this sub distributed ledger is related to a vendor subscription channel. The distributed ledger of the main subscription channel is updated with provisioning of the resources. An authenticity view is provided as to updated main subscription channel.

System and method of provisioning transparency using trusted secure chained visibility with customer conscience

Techniques for managing containerized workloads in a container computing environment are disclosed. For example, a method comprises the following steps. In a container computing environment configured to create an instance of a containerized workload for executing a microservice, the method computes a parameter based on a first set of execution conditions for the microservice, wherein the parameter represents a resource utilization value at which at least one additional instance of the containerized workload is created for executing the microservice. The method then re-computes the parameter based on a second set of execution conditions for the microservice.

Containerized workload management in container computing environment

Techniques for management of a multi-cloud computing environment are disclosed. For example, a method comprises the following steps. A node is configured to participate in a distributed ledger protocol with one or more other nodes. The node is further configured to maintain, in conjunction with the one or more other nodes, a data structure of blocks, wherein the blocks represent one or more of elements and operations associated with a multi-cloud computing environment and collectively function as a digital representation of the multi-cloud computing environment.

Blockchain twin architecture for secure management of multi-cloud computing environment

Methods, apparatus, and processor-readable storage media for identifying application relationships using natural language processing techniques are provided herein. An example method includes processing an input related to a software project to derive a set of keywords and a set of intents; identifying a first application, from among a plurality of applications, related to the input based on a comparison of subsets of the keywords to keywords that are mapped to at least some of the plurality of applications in a first knowledge base; identifying a second application from among the plurality of applications related to the first application for at least a portion of the intents, wherein the second application is identified using a second knowledge base that maps the second application to the first application and at least one intent from the set of intents; and controlling initiation of an automated action based on the identified second application.

Identifying application relationships using natural language processing techniques

An apparatus comprises at least one processing device configured to register two or more source services, wherein registering a given source service comprises executing the given source service to identify inputs and outputs of the given source service, and to select a set of outputs from the registered source services for generating at least a portion of a user interface of an application in an application client, the selected set of outputs comprising at least first and second output from first and second ones of the registered source services. The processing device is also configured to determine an orchestration of the registered source services for obtaining the selected set of outputs, to generate a custom microservice configured to execute the registered source services in accordance with the determined orchestration, and to deploy the custom microservice in an auto-scalable computing framework accessible to the application client of the application.

Generation and deployment of custom microservices for orchestration of multiple source services

Methods, apparatus, and processor-readable storage media for automated subscription management for remote infrastructure are provided herein. An example computer-implemented method includes obtaining subscription data related to at least one subscription for hardware infrastructure provided by a service provider, wherein the subscription data is obtained by the hardware infrastructure at a remote location; obtaining usage data and deployment data for the hardware infrastructure that is associated with the at least one subscription; processing the subscription data, the usage data and the deployment data using an artificial intelligence-based framework to identify a time period for initiating performance of one or more actions to automatically adjust the at least one subscription, wherein the artificial intelligence-based framework identifies the time period based on historical subscription data related to one or more other subscriptions; and causing the performance of the one or more actions to be initiated within the identified time period.

Automated subscription management for remote infrastructure

Techniques are disclosed for automated content generation in a computing environment. For example, a method comprises managing one or more pipelines respectively corresponding to one or more item lifecycle stages. Each pipeline of the one or more pipelines comprises a set of data processing elements configured to execute a set of operations associated with a corresponding one of the item lifecycle stages. At least a portion of the set of operations executed in each pipeline comprise one or more content generation operations configured to generate a content fragment to automatically document the corresponding item lifecycle stage. The method then builds a document from at least a portion of content fragments generated by the one or more pipelines.

Automated content generation in computing environment

Growing community of inventors

Bengaluru, India

Shibi Panikkar