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:
Jun. 01, 2021

Filed:

Jan. 17, 2019
Applicant:

Vmware, Inc., Palo Alto, CA (US);

Inventors:

Arnak Poghosyan, Yerevan, AM;

Clement Pang, Palo Alto, CA (US);

Ashot Nshan Harutyunyan, Yerevan, AM;

Naira Movses Grigoryan, Yerevan, AM;

Assignee:

VMware, Inc., Palo Alto, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 15/173 (2006.01); G06F 11/34 (2006.01); G06F 11/30 (2006.01); G06F 11/07 (2006.01); H04L 12/24 (2006.01); G06F 16/2458 (2019.01); G06F 17/18 (2006.01); G06N 3/02 (2006.01);
U.S. Cl.
CPC ...
G06F 11/3452 (2013.01); G06F 11/0793 (2013.01); G06F 11/3006 (2013.01); G06F 11/3419 (2013.01); G06F 16/2474 (2019.01); G06F 16/2477 (2019.01); G06F 17/18 (2013.01); G06N 3/02 (2013.01); H04L 41/142 (2013.01); H04L 41/147 (2013.01); G06F 2201/88 (2013.01);
Abstract

Computational processes and systems are directed to forecasting time series data and detection of anomalous behaving resources of a distributed computing system data. Processes and systems comprise off-line and on-line modes that accelerate the forecasting process and identification of anomalous behaving resources. In the off-line mode, recurrent neural network ('RNN') is continuously trained using time series data associated with various resources of the distributed computing system. In the on-line mode, the latest RNN is used to forecast time series data for resources in a forecast time window and confidence bounds are computed over the forecast time window. The forecast time series data characterizes expected resource usage over the forecast time window so that usage of the resource may be adjusted. The confidence bounds may be used to detect anomalous behaving resources. Remedial measures may then be executed to correct problems indicated by the anomalous behavior.


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