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.

Date of Patent:
Dec. 01, 2020

Filed:

Jul. 20, 2018
Applicant:

Emc Ip Holding Company Llc, Hopkinton, MA (US);

Inventors:

Vinícius Michel Gottin, Rio de Janeiro, BR;

Alex Laier Bordignon, Niteróri, BR;

Assignee:

EMC IP Holding Company LLC, Hopkinton, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/02 (2006.01); G06N 3/00 (2006.01); G06N 3/08 (2006.01); G06F 9/48 (2006.01); G06F 9/50 (2006.01); G06F 9/455 (2018.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06N 3/02 (2013.01); G06F 9/455 (2013.01); G06F 9/45533 (2013.01); G06F 9/48 (2013.01); G06F 9/4806 (2013.01); G06F 9/4843 (2013.01); G06F 9/4881 (2013.01); G06F 9/50 (2013.01); G06F 9/5005 (2013.01); G06F 9/5027 (2013.01); G06F 9/5038 (2013.01); G06F 9/5061 (2013.01); G06F 9/5077 (2013.01); G06N 3/00 (2013.01); G06N 3/0481 (2013.01); G06N 3/08 (2013.01); G06F 2009/45575 (2013.01); G06F 2009/45591 (2013.01);
Abstract

Techniques are provided for predicting a time-to-finish of at least one workflow in a shared computing environment using a deep neural network with a biangular activation function. An exemplary method comprises: obtaining a specification of an executing workflow of multiple concurrent workflows in a shared computing environment, wherein the specification comprises states of past executions of the executing workflow; obtaining a trained deep neural network, wherein the trained deep neural network is trained to predict one or more future states of the executing workflow using the states of past executions and wherein the trained deep neural network employs a biangular activation function comprising multiple parameters that define a position and a slope associated with two angles of the biangular activation function for a range of input values; and estimating, using the at least one trained deep neural network, a time-to-finish of the executing workflow of the multiple concurrent workflows.


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