Abnormal load detection method for cloud calculation on-line business

A load detection and cloud computing technology, applied in the field of cloud computing applications, can solve the problems of inability to obtain high accuracy, difficult to automate, and high false alarm rate

Active Publication Date: 2015-11-18
TSINGHUA UNIV
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  • Abstract
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  • Claims
  • Application Information

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

Method used

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  • Abnormal load detection method for cloud calculation on-line business
  • Abnormal load detection method for cloud calculation on-line business
  • Abnormal load detection method for cloud calculation on-line business

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Embodiment Construction

[0068] An abnormal load detection method for cloud computing online services proposed by the present invention uses historical load data of online services to detect abnormal loads of online services through wavelet analysis and statistical analysis. The method is described in detail in conjunction with drawings and embodiments as follows.

[0069] The method process that the present invention proposes is as figure 1 shown, including the following steps:

[0070] Step 1) Use the fixed-period sampling method to collect the information data of each load item of all hosts carrying an online service, mainly including CPU usage, memory usage, disk I / O rate and network I / O rate, recorded as in Indicates the load statistics data at a certain time point i, i=1,2,...,n; n is a positive integer; x indicates the use of any one of the host's CPU, memory, disk I / O or network I / O Rate;

[0071] In this embodiment, CPU usage is used for illustration. For the CPU usage, the default is...

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Abstract

The invention relates to an abnormal load detection method for cloud calculation on-line business, and belongs to the technical field of cloud calculation application. According to the method, information data of various load items of all hosts of a certain on-line business is collected and born by employing a fixed cycle sampling method; the information data of each load item of the current on-line business is processed to time sequences with fixed time intervals, and the time sequences of all load item data of the current business are obtained; discrete wavelet transformation of each time sequence of the on-line business is performed, statistics and analysis of each coefficient vector of an obtained coefficient matrix are conducted, and the probability of the abnormal load is calculated; the obtained probability and a confidence interval given by a confidence function are compared, and whether the abnormal load exists is determined; and a bearing server which is abnormal in the current on-line business is searched by employing the K-means clustering algorithm. Compared with the conventional method, according to the abnormal load detection method, higher accuracy can be obtained, and the adaptability is better.

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 utilizing 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 services to platforms based on cloud architecture. Using cloud computing technology, resources such as computing, storage, and network allocated to specific businesses can be increased or decreased as needed, thereby maximizing resource utilization and reducing business operating costs. Online business accounts for a large proportion of all businesses deployed on cloud platforms. Since online businesses often directly provide service interfaces for users, the load of online businesses is more likely to be affected by the number of user visits. Monitoring the business load is the b...

Claims

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Application Information

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IPC IPC(8): H04L12/26H04L29/08
CPCH04L43/08H04L67/10H04L41/142H04L43/022G06F11/3452H04L67/1008H04L67/1023G06F11/2066G06F11/3433
Inventor 周悦芝刘金钊张迪张尧学
Owner TSINGHUA UNIV
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