The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Apr. 26, 2022
Filed:
Aug. 16, 2018
Emc Ip Holding Company Llc, Hopkinton, MA (US);
Jonas F. Dias, Rio de Janeiro, BR;
Angelo Ciarlini, Rio de Janeiro, BR;
Romulo D. Pinho, Niteroi, BR;
Vinicius Gottin, Rio de Janeiro, BR;
Andre Maximo, Rio de Janeiro, BR;
Edward Pacheco, Rio de Janeiro, BR;
David Holmes, Guildford, GB;
Keshava Rangarajan, Sugar Land, TX (US);
Scott David Senften, Sugar Land, TX (US);
Joseph Blake Winston, Houston, TX (US);
Xi Wang, Houston, TX (US);
Clifton Brent Walker, Richmond, TX (US);
Ashwani Dev, Katy, TX (US);
Chandra Yeleshwarapu, Sugar Land, TX (US);
Nagaraj Srinivasan, Sugar Land, TX (US);
EMC IP HOLDING COMPANY LLC, Hopkinton, MA (US);
Abstract
A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric based on the deep neural network output.