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:
Jul. 07, 2020

Filed:

Jan. 23, 2018
Applicant:

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

Inventors:

Debessay Fesehaye Kassa, Mountain View, CA (US);

Lenin Singaravelu, Sunnyvale, CA (US);

Xiaobo Huang, Palo Alto, CA (US);

Amitabha Banerjee, Belmont, CA (US);

Ruijin Zhou, Palo Alto, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/28 (2019.01); G06F 11/30 (2006.01); H04L 12/24 (2006.01); G06F 11/34 (2006.01); H04L 29/08 (2006.01);
U.S. Cl.
CPC ...
G06F 16/285 (2019.01); G06F 11/3006 (2013.01); G06F 11/3051 (2013.01); G06F 11/3433 (2013.01); G06F 11/3452 (2013.01); G06F 11/3476 (2013.01); G06F 16/283 (2019.01); H04L 41/0813 (2013.01); H04L 67/1097 (2013.01);
Abstract

Certain embodiments described herein are generally directed to improving performance of one or more machines within a system by clustering multidimensional datasets relating to the performance of the machines using inter-group dissimilarities between groups of the dataset. The method for improving performance of one or more machines within a system, includes forming a multidimensional dataset having a plurality of groups using performance related data associated with one or more machines in the system, clustering the plurality of groups into one or more clusters based on intergroup dissimilarities between the plurality of groups, identifying one or more anomalous clusters from among the one or more clusters, identifying the one or more anomalous groups in the one or more anomalous clusters, and adjusting a configuration of the system to improve the performance of the one or more machines corresponding to the one or more anomalous groups.


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