Method for quickly quantitative grading of large-scale heterogeneous cluster nodes at cloud data center

A cloud data center and heterogeneous cluster technology, applied to electrical components, transmission systems, etc., to achieve the effect of simple and effective algorithms, low complexity, and high accuracy

Active Publication Date: 2017-03-01
SOUTHEAST UNIV +1
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Problems solved by technology

[0004] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a rapid quantitative classification method for large-scale heterogeneous cluster nodes

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  • Method for quickly quantitative grading of large-scale heterogeneous cluster nodes at cloud data center
  • Method for quickly quantitative grading of large-scale heterogeneous cluster nodes at cloud data center
  • Method for quickly quantitative grading of large-scale heterogeneous cluster nodes at cloud data center

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Embodiment Construction

[0029] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention All modifications of the valence form fall within the scope defined by the appended claims of the present application.

[0030] A rapid quantitative classification method for large-scale heterogeneous cluster nodes in cloud data centers, such as figure 1 shown, including the following steps:

[0031] Step 1. Collect the performance parameters of each cluster node to be classified, calculate the average value and standard deviation of each performance parameter of all cluster nodes, and standardize the original data, and then use the extreme value standardization method to compress the ...

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Abstract

The invention discloses a method for quickly quantitative grading of large-scale heterogeneous cluster nodes at a cloud data center, and the method logically comprises three main parts: cluster node performance parameter preprocessing, cluster node performance parameter matrix calibration, and cluster node performance parameter soft clustering. The method comprises the steps: firstly carrying out the collection quantification and standardization processing of performance parameters of different dimensions of cluster nodes at the cloud data center; secondly carrying out the calibration of the cluster node performance parameter values after standardization, introducing a similarity coefficient method based on the calibration values, and building a performance parameter fuzzy similarity matrix of all cluster nodes at the cloud data center; reconstructing the obtained fuzzy similarity matrix based on a transitive closure method, enabling the fuzzy similarity matrix to become a fuzzy equivalent matrix, carrying out the intercepting of the fuzzy equivalent matrix at a proper intercept level, and finally obtaining a large-scale cluster node performance parameter cluster graph. The method provides a node performance reference basis for the subsequent data layout and energy consumption management of the cloud data center.

Description

technical field [0001] The invention relates to the field of cloud computing platform and data center management, in particular to cluster management technology, and in particular to a rapid quantitative classification method for large-scale heterogeneous cluster nodes in a cloud data center. Background technique [0002] With the development of cloud computing technology, in order to ensure good availability, reliability and scalability of cloud services on a global scale, existing cloud service providers often establish multiple large data centers around the world, and configure private networks or By renting high-bandwidth capacity links from network service providers, data centers around the world are interconnected into a unified cloud platform. The cloud platform can provide a wealth of computing and storage capabilities, enabling enterprises and scientific research institutions to process and analyze big data more conveniently and effectively, mainly due to the advanc...

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Application Information

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IPC IPC(8): H04L29/08
CPCH04L67/10H04L67/1097
Inventor 熊润群罗军舟东方金嘉晖
Owner SOUTHEAST UNIV
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