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Docker container fault intelligent prediction method based on time sequence evolution gene

A docker container and intelligent prediction technology, applied in the field of communication, can solve the problems of passive system maintenance, large manpower consumption, low system maintenance efficiency, etc., and achieve the effect of improving maintenance level, ensuring timeliness and accuracy

Pending Publication Date: 2020-11-13
上海伽易信息技术有限公司
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AI Technical Summary

Problems solved by technology

[0004] (1) The network management system of the PaaS platform does not have the intelligent fault analysis function. When the business is abnormal, the maintenance personnel need to spend a lot of manpower to check the fault and find the cause of the fault, and the system maintenance efficiency is low.
[0005] (2) The network management system of the PaaS platform does not have an intelligent fault warning function. Once a serious fault occurs, it will affect the normal operation of the system, and the system maintenance is relatively passive.

Method used

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  • Docker container fault intelligent prediction method based on time sequence evolution gene
  • Docker container fault intelligent prediction method based on time sequence evolution gene
  • Docker container fault intelligent prediction method based on time sequence evolution gene

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Embodiment 1

[0058] figure 1 Flowchart for deep learning data processing, figure 2 It is a schematic diagram of the principle of the present invention, such as Figure 1-Figure 2 shown. An intelligent prediction method for Docker container faults based on time series evolution genes, including:

[0059] The deep learning data processing flow of the present invention is as follows:

[0060] A kind of Docker container failure intelligent prediction method based on time series evolution gene of the present invention is as follows:

[0061] (1) Segment the multi-dimensional time series into many segments;

[0062] (2) Clustering the fragments to find typical patterns;

[0063] (3) For different modes, use conditional confrontation network (CVAE-GAN) to capture its distribution characteristics;

[0064] (4) Combining changes in distribution characteristics over time to predict upcoming anomalies.

[0065] Further, include the following steps:

[0066] Step 1: Collect K8S component logs...

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Abstract

The invention discloses a Docker container fault intelligent prediction method based on a time sequence evolution gene, and the method comprises the steps: carrying out the segmentation of a multi-dimensional time sequence, and dividing the multi-dimensional time sequence into a plurality of segments; clustering the segments to discover a typical mode; for different modes, adopting a conditional adversarial network (CVAE-GAN) to capture distribution characteristics of the modes; and combining the change of the distribution characteristics along with time, and predicting an imminent anomaly. The maintenance level of the Docker of the cloud platform is effectively improved.

Description

technical field [0001] The present invention relates to an intelligent prediction method for Docker container faults based on timing evolution genes, in particular to a method for intelligent prediction of Docker container faults based on timing evolution genes for PaaS platform operation and maintenance. The invention belongs to the field of communication. Background technique [0002] The traditional operation and maintenance mode of the PaaS platform is that after the network management system detects a device alarm, it notifies the maintenance personnel to carry out maintenance. This is a manual repair after the event, and the fault response time is long, which cannot meet the high real-time business requirements. System operation and maintenance personnel spend most of their time and energy dealing with some simple and repetitive problems. The physical labor is too heavy, the work efficiency is low, and a large amount of maintenance resources need to be invested. [000...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24
CPCH04L41/142H04L41/147H04L41/0631Y02D10/00
Inventor 沙泉
Owner 上海伽易信息技术有限公司
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