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:
Jan. 07, 2020

Filed:

Sep. 02, 2015
Applicant:

China Unionpay Co., Ltd., Shanghai, CN;

Inventors:

Hongfeng Chai, Shanghai, CN;

Chengrong Wu, Shanghai, CN;

Jinzhi Hua, Shanghai, CN;

Zhenfang Wang, Shanghai, CN;

Yang Yang, Shanghai, CN;

Jun Zhang, Shanghai, CN;

Assignee:

CHINA UNIONPAY CO., LTD., Shanghai, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 9/50 (2006.01); G06F 9/455 (2018.01); G06F 9/46 (2006.01);
U.S. Cl.
CPC ...
G06F 9/45558 (2013.01); G06F 9/5077 (2013.01); G06F 9/46 (2013.01); G06F 9/5083 (2013.01); G06F 2009/4557 (2013.01);
Abstract

The application discloses a load evaluation system for virtual machines in a cloud environment, comprising: a monitored data collecting module which is configured to collect monitored data from one or more virtual machines in a cloud data center; a monitored data processing module which is coupled with the monitored data collecting module and is configured to: perform aggregated grouping on the monitored data by using a first KMeans algorithm for each of the one or more virtual machines, each group having a center point coordinate, and calculate a performance characteristic of each virtual machine of the one or more virtual machines according to the grouping and the center point coordinates of individual groups, i.e., a first characteristic value; perform aggregated grouping on the one or more virtual machines using a second KMeans algorithm based on the first characteristic value of each virtual machine, thus determining the characteristic type and performance load value of each virtual machine of the one or more virtual machines. The application also discloses a load evaluation method for virtual machines in a cloud environment as well as a service node.


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