Dell Products Inc.

Emc IP Holding Company LLC

Seshadri Srinivasan

Hung The Dinh

Sashibhusan Panda

Shishir Kumar Parhi

Shivangi Geetanjali

Baishali Roy

Antarlina Tripathy

Sweta Kumari

Bijan Kumar Mohanty

Sambasivarao Gaddam

Shivangi Maharana

Harikrishna Reyyi

Sowmya Kumar

Panguluru Vijaya Sekhar

Kiran Kumar Pidugu

Sourav Datta

Lakshman Kumar Tiwari

Puneet Kumar Arora

Sabu K Syed

Satish Ranjan Das

Rajesh Krishnan

Manikandan Pammal Rathinavelu

Krishna Mohan Akkinapalli

Vijaya Panguluru Sekhar

Geetha Venkatesan

Muhammed Mohiuddin

Ranjani Muthyam Venkata

Sowmya K

Bharath Alla

Yash Khare

Shashikiran Rajagopal

A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about at least one of respective protocols and respective interfaces associated with respective ones of the plurality of components. The data is analyzed to determine at least one of the respective protocols and the respective interfaces associated with the respective ones of the plurality of components. In the method, operations of one or more components of the plurality of components are tested based at least in part on the determination of the at least one of the respective protocols and the respective interfaces. The method further includes outputting respective statuses of the one or more components, wherein the respective statuses are derived at least in part from the testing.

Testing framework with load forecasting

A method comprises receiving a request for validation of software comprising one or more applications, analyzing the request and generating one or more validation steps based at least in part on the analysis. In the method, a time to complete the one or more validation steps is predicted. The predicting is performed using one or more machine learning models, and is based at least in part on a type and a number of the one or more applications.

Software validation framework

A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about one or more issues with the plurality of components. The data is analyzed and categorized based at least in part on the analysis. In the method, one or more application programming interfaces (APIs) are selected to monitor respective statuses of the plurality of components, wherein the selection is based at least in part on the categorization of the data, and the data is pushed to the one or more APIs.

Component monitoring framework with predictive analytics

A method comprises managing multiple tasks of multiple entities associated with a deployment of a software program with a deployment framework comprising a machine learning module configured to assist with managing the multiple tasks of the multiple entities. The managing step comprises tracking a status of one or more of the multiple tasks, and predicting a time taken for a given one of the multiple entities to complete a given one of the multiple tasks.

Enterprise deployment framework with artificial intelligence/machine learning

Methods, apparatus, and processor-readable storage media for transaction visibility framework implemented using artificial intelligence are provided herein. An example computer-implemented method includes monitoring data related to a transaction flow across multiple layers of an enterprise system; determining which of the multiple layers correspond to at least one detected problem within the transaction flow by applying artificial intelligence techniques to the data; generating a visualization of the transaction flow, wherein generating the visualization comprises generating a sequential view of the transaction flow, generating one or more parallel sub-transaction flows pertaining to one or more dependencies of the transaction flow, and producing a visual indication of the detected problem; determining, based on analyzing the generated visualization, one or more automated actions related to the detected problem and the determined layer of the enterprise system corresponding to the detected problem; and automatically initiating the one or more automated actions.

Transaction visibility frameworks implemented using artificial intelligence

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Shrewsbury, MA, United States of America

Seshadri Srinivasan