A Dynamic Monitoring Interval Adjustment Method Based on Naive Bayesian Classification Technique
A technology of Bayesian classification and dynamic monitoring, applied in hardware monitoring, instruments, character and pattern recognition, etc., can solve problems such as information redundancy and low resource utilization, and achieve improved operating efficiency, high accuracy, and adjusted monitoring intervals Effect
Active Publication Date: 2018-03-16
ZHEJIANG UNIV +1
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[0006] In view of the above-mentioned technical defects, the purpose of the present invention is to address the problems of information redundancy and low resource utilization in the fixed monitoring time interval model of traditional monitoring software, adding the function of dynamically changing system monitoring configuration during operation to the cloud monitoring system, and proposing a The dynamic monitoring interval adjustment method based on naive Bayesian classification technology makes trend predictions for the current state based on historical monitoring information, and judges the system state based on the predicted results, so as to dynamically adjust the monitoring of monitoring time, which can be effective Adjust the monitoring interval and optimize the frequency of information transmission to achieve efficient operation of the system
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[0023] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.
[0024] The present invention uses the basic data in the monitoring information: CPU and memory information to predict and classify the system status, and the method is designed figure 2 According to the monitoring interval adjustment structure, the corresponding monitoring interval adjustment operation is performed according to the classification results. Some relevant definitions are first given below.
[0025] CPU usage and memory usage, because these two indicators have various types of data in the system. For example, in the information displayed by using the top command in the linux system, the indicators in the CPU column mainly include the following: user mode ( user), system mode (system), idle usage (idle), I / O waiting (iowait), etc. The column of memory data in the Top command shows options such as total (total memory), used (used)...
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The invention discloses a dynamic monitoring interval adjustment method based on naive Bayesian classification technology, which includes obtaining monitoring information of the host from the monitoring target node host through the monitoring system client Agent, storing the acquired monitoring information, and performing data processing. Preprocessing, eliminating abnormal and invalid data; setting the classification feature set to obtain classification results; processing the classification results of monitoring information at the current time point, the method provided by the invention can deal with the monitoring problems in the complex environment of cloud computing, and provide accurate and efficient The monitoring solution improves the operating efficiency of the monitoring system, enables it to adapt to load changes caused by elastic scaling in the cloud computing environment, and effectively adjusts the monitoring interval.
Description
technical field [0001] The invention relates to the technical field of cloud computing intelligent monitoring systems, in particular to a method for adjusting dynamic monitoring intervals based on naive Bayesian classification technology. Background technique [0002] Cloud monitoring is accompanied by the operation of the entire cloud computing system. The operations performed are not only simple operations such as adding and deleting monitoring targets, and obtaining data. Users can customize data processing behavior according to their own needs. The monitored historical data can be regarded as a kind of log data of system information, and the log analysis can be used to obtain the historical operation status and trend of the system, so as to make further decisions. [0003] A cloud computing system is usually a large-scale system. Taking OpenStack as an example, each component in the system can be regarded as an independent module, and there is frequent information inter...
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IPC IPC(8): G06F11/30G06F11/34G06K9/62
Inventor 尹建伟陈怡东赵新奎李莹邓水光
Owner ZHEJIANG UNIV