Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Feature-based load selecting method and system

A feature selection and feature data technology, applied in the field of algorithms, can solve the problem of low load selection efficiency and achieve the effect of improving efficiency

Active Publication Date: 2015-07-08
ZHEJIANG UNIV
View PDF1 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of this, the present invention provides a method and system for selecting loads according to characteristics to solve the technical problem of low load selection efficiency in the prior art

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Feature-based load selecting method and system
  • Feature-based load selecting method and system
  • Feature-based load selecting method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0021] Such as figure 1 Shown is a flowchart of a method for selecting a load based on features according to an embodiment of the present invention, and the method includes the following steps:

[0022] Step S101, performing preprocessing on the feature data to be processed.

[0023] In the embodiment of the present invention, preprocessing is performed on the feature data to be processed. Through the preprocessing, the effective data of the feature data can be obtained. The feature data is the feature displayed by the load during operation, and the feature includes but is not limited to: CPU-intensive, memory-intensive, IO-intensive and network-intensive. The step of preprocessing the feature data to be processed includes:

[0024] 1. Composing the feature data to be processed into a data matrix through granularity selection.

[0025] In the embodiment of the present invention, the granularity of the feature data to be processed is selected, that is, take the feature data ...

Embodiment 2

[0051] Such as figure 2 Shown is the structural diagram of the load system selected according to the characteristics provided by the embodiment of the present invention. For the convenience of description, only the parts related to the embodiment of the present invention are shown, including:

[0052] The preprocessing unit 201 is configured to preprocess the feature data to be processed.

[0053] In the embodiment of the present invention, preprocessing is performed on the feature data to be processed. Through the preprocessing, the effective data of the feature data can be obtained. The feature data is the feature displayed by the load during operation, and the feature includes but is not limited to: CPU-intensive, memory-intensive, IO-intensive and network-intensive. The preprocessing unit 201 includes:

[0054] The data matrix composing subunit 2011 is configured to compose the feature data to be processed into a data matrix through granularity selection.

[0055]In th...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention applies to the field of algorithms, and provides a feature-based load selecting method and system. The method comprises the steps of preprocessing feature data to be processed; classifying the feature data to be processed through the feature clustering algorithm; acquiring representative element of each class; selecting high-accuracy load corresponding to the feature according to the mutual information and the representative elements. According to the method, the feature data to be processed are preprocessed and then classified through the feature clustering algorithm to obtain the representative element of each class, and then the high-accuracy load corresponding to the feature can be selected according to the mutual information value and the representative elements; the method and the system are high in efficiency and can increase the load selection efficiency.

Description

technical field [0001] The invention belongs to the field of algorithms, and in particular relates to a method and system for selecting loads according to characteristics. Background technique [0002] Whether it is a traditional physical machine or a virtual cluster in cloud computing, it is very important for system optimization. In order to adapt to different application requirements, different optimization methods will be adopted for system optimization. In this case, it is first necessary to classify the loads of physical machines or virtual machines, and adopt different optimization methods according to whether they are CPU-intensive, memory-intensive, IO-intensive, or network-intensive to improve efficiency. [0003] The load classification method is the premise of system optimization, and its efficiency directly affects the efficiency of system optimization. In the process of load classification, accuracy and efficiency are mutually restrictive factors, and usually...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06F17/30
Inventor 尹建伟林鹏翔赵新奎李莹邓水光吴健吴朝晖
Owner ZHEJIANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com