Monitoring data intelligent sampling method based on relevancy analysis

A technology of monitoring data and correlation analysis, applied in hardware monitoring and other directions, can solve the problems of increasing the sampling rate of useless data, low efficiency, reducing sampling efficiency, etc., and achieve the effect of reducing useless data collection, reducing collection, and maintaining accuracy.

Active Publication Date: 2017-09-05
ZHEJIANG UNIV +1
View PDF2 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The extreme case is the method of full sampling. Because there is no screening, the proportion of useless data is very objective and the efficiency is very low.
[0007] In general, reducin

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
  • Monitoring data intelligent sampling method based on relevancy analysis
  • Monitoring data intelligent sampling method based on relevancy analysis
  • Monitoring data intelligent sampling method based on relevancy analysis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0037] Such as figure 1 As shown, the monitoring data intelligent sampling method based on correlation analysis of the present invention comprises the following steps:

[0038] (1) Time series data encoding.

[0039] Time series data encoding is a character stream structure that can be mined by the Apriori algorithm after encoding continuous and original data with specified rules, specifically as figure 2 shown.

[0040] 1.1 Data normalization: Use the following formula to normalize the data, retain the relative size and trend of the data, and remove the influence of the absolute size of the data on the algorithm.

[0041]

[0042] where: V min is the minimum value of V, V max is the maximum value of V, after calculating RV i The range of is [0,...

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 discloses a monitoring data intelligent sampling method based on relevancy analysis, which includes the four key steps: time series data encoding, relevance relationship mining, state transition matrix calculation, and state prediction. According to the method, a monitoring cycle can be dynamically adjusted according to the prediction on future resource usage of a main unit, thereby reducing sampling frequency while resource usage varies stably, and increasing the sampling frequency while the resource usage varies sharply to save computing and storage resources. Compared with the prior art, the method has the advantages that the monitoring cycle can be enlarged and sampling frequency can be decreased while a machine runs stably; when the machine running fluctuates, it is required to decrease the monitoring cycle and increase sampling rate; more meaningful monitoring data are acquired, the quantity of gibberish to be collected is decreased effectively, spending major computing resources to on gibberish acquisition, computing and other processing is avoided, efficiency is improved, and high accuracy is maintained while gibberish collection is reduced.

Description

technical field [0001] The invention belongs to the technical field of intelligent sampling, and in particular relates to an intelligent sampling method for monitoring data based on correlation analysis. Background technique [0002] With the further popularization and in-depth application of cloud computing and mobile Internet, various network applications and services are playing a more important role in various industries. Some network services are sensitive to load fluctuations. A reasonably designed sampling rate algorithm can ensure low overhead in the use of resources such as the network in the host machine, and at the same time reduce the pressure on computing resources, especially storage resources, at the back end of the monitoring system, and for Key information is not lost, so it is a key issue in the direction of system performance optimization, and its efficiency directly affects the efficiency of system optimization. The current sampling methods are roughly d...

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): G06F11/30
Inventor 尹建伟吴昊邓水光李莹吴健吴朝晖易峥
Owner ZHEJIANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap