Monitoring data intelligent sampling method based on relevancy analysis

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...

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, reducing the monitoring period and increasing the sampling frequency can improve the sampling accuracy, but it will reduce the sampling efficiency and increase the sampling rate of useless data

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