An abnormal load detection method for cloud computing online business

Abstract

The invention relates to an abnormal load detection method for cloud computing online services, which belongs to the field of cloud computing application technology. The method uses a fixed-period sampling method to collect information data of each load item of all hosts carrying an online service; for the current The information data of each load item of the online business is processed into a time series with a fixed time interval, and the time series of all load item data of the current business is obtained; the discrete wavelet transform is performed on each time series of the online business, and the obtained Perform statistical analysis on each coefficient vector of the coefficient matrix to calculate the probability of abnormal load; compare the obtained probability with the confidence interval given by the confidence function to determine whether there is abnormal load; use K-means clustering algorithm to find Find out the bearer server where the current online business is abnormal. Compared with existing methods, this method not only can obtain higher accuracy, but also has better adaptive ability.

Description

Technical field [0001] The invention belongs to the technical field of cloud computing applications, and in particular relates to a method for identifying abnormal loads and abnormal operating conditions of services by using historical load data of online services. Background technique [0002] With the development of cloud computing technology, more and more users choose to deploy or migrate their services to platforms based on cloud architecture. Using cloud computing technology, the computing, storage, and network resources allocated to specific services can be increased or decreased as needed, thereby maximizing resource utilization and reducing business operating costs. Online businesses account for a large proportion of all businesses deployed on cloud platforms. Since online services often provide users with a service interface directly, the load of online services is more susceptible to user visits. Monitoring the business load is the basis for cloud computing to provid...

AI Technical Summary

Problems solved by technology

Since the characteristics of the business are constantly changing throughout the life cycle of the business, these "fingerprints" also need to be constantly adjusted and corrected, making it difficult to achieve complete automation

[0009] To sum up, the existing methods of using load data to judge the abnormal conditions of online services cannot obtain high accuracy, and there is a problem of high false alarm rate

At the same time, these methods rely to a large extent on the experience of operation and maintenance personnel, and cannot achieve complete automatic monitoring

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