CN111880981A - Fault repairing method and related device for docker container - Google Patents
Fault repairing method and related device for docker container Download PDFInfo
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- CN111880981A CN111880981A CN202010752683.1A CN202010752683A CN111880981A CN 111880981 A CN111880981 A CN 111880981A CN 202010752683 A CN202010752683 A CN 202010752683A CN 111880981 A CN111880981 A CN 111880981A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
- G06F11/2252—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing using fault dictionaries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
- G06F11/26—Functional testing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
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Abstract
The application discloses a fault repairing method of a docker container, which comprises the following steps: collecting fault data, and performing feature selection and feature extraction on the fault data to obtain fault data with common features; carrying out data preprocessing on the fault data with the common characteristics; constructing a fault analysis model according to fault data subjected to data preprocessing and a machine learning algorithm; collecting fault information of the docker container, and analyzing the fault information of the docker container through the fault analysis model to obtain a corresponding fault type; and carrying out corresponding fault processing according to the fault type. The method can realize automatic positioning and processing of container faults, reduce the workload of personnel and save the cost. The application also discloses a fault repairing device, equipment and a computer readable storage medium of the docker container, which have the technical effects.
Description
Technical Field
The application relates to the technical field of computers, in particular to a fault repairing method for a docker container; it also relates to a fault-repairing device, an apparatus and a computer-readable storage medium for a docker container.
Background
Docker is a LXC-based advanced container engine for the PaaS provider dotCloud open source, with source code hosted on gitubs, open source based on the go language and compliant with the apache2.0 protocol. In recent years, Docker has been widely paid attention to and applied, and the application status of Docker is well reflected in code activity of gitubs, integration of Redhat in RHEL6.5 and the like. In the operation process of the docker container, various error information such as operation errors, container errors and the like inevitably occurs, at this time, an engineer is required to spend a large amount of work to perform fault location and fault treatment, and particularly when the fault location and the fault treatment are required to be performed in different places, the engineer is required to go to a fault site to solve the fault, so that traveling and development cost are increased.
In view of the above, how to save cost and effectively process failure in time has become a technical problem to be solved by those skilled in the art.
Disclosure of Invention
The application aims to provide a fault repairing method for a docker container, which can realize automatic positioning and processing of container faults, reduce the workload of personnel and save the cost. Another object of the present application is to provide a fault repairing apparatus, a device and a computer readable storage medium for a docker container, all having the above technical effects.
In order to solve the technical problem, the present application provides a fault repairing method for a docker container, including:
collecting fault data, and performing feature selection and feature extraction on the fault data to obtain fault data with common features;
carrying out data preprocessing on the fault data with the common characteristics;
constructing a fault analysis model according to fault data subjected to data preprocessing and a machine learning algorithm;
collecting fault information of the docker container, and analyzing the fault information of the docker container through the fault analysis model to obtain a corresponding fault type;
and carrying out corresponding fault processing according to the fault type.
Optionally, the performing data preprocessing on the fault data with the common characteristics includes:
and carrying out data cleaning, data integration, data exchange and data specification processing on fault data with common characteristics.
Optionally, the performing feature selection and feature extraction on the fault data includes:
and performing feature selection and feature extraction on the fault data by adopting any one of a filtering method, a wrapping method and an embedding method.
Optionally, the collecting fault data includes:
fault data is collected from an error log of a device or system.
Optionally, the method further includes:
and when the docker container fails, performing fault alarm.
In order to solve the above technical problem, the present application further provides a fault repairing apparatus for a docker container, including:
the data acquisition module is used for acquiring fault data and performing feature selection and feature extraction on the fault data to obtain fault data with common features;
the data preprocessing module is used for preprocessing the fault data with the common characteristics;
the model building module is used for building a fault analysis model according to the fault data subjected to data preprocessing and a machine learning algorithm;
the fault type analysis module is used for acquiring fault information of the docker container and analyzing the fault information of the docker container through the fault analysis model to obtain a corresponding fault type;
and the fault processing module is used for carrying out corresponding fault processing according to the fault type.
Optionally, the data preprocessing module includes:
the data cleaning unit is used for carrying out data cleaning processing on the fault data;
the data integration unit is used for carrying out data integration processing on the fault data;
the data exchange unit is used for carrying out data exchange processing on the fault data;
and the data specification unit is used for carrying out data specification processing on the fault data.
Optionally, the data acquisition module is specifically configured to perform feature selection and feature extraction on the fault data by using any one of a filtering method, a wrapping method, and an embedding method.
In order to solve the above technical problem, the present application further provides a docker container fault repair apparatus, including:
a memory for storing a computer program;
a processor for implementing the steps of the fault repair method for a docker container as described in any of the above when executing the computer program.
In order to solve the above technical problem, the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the fault repairing method for a docker container as described in any one of the above.
The fault repairing method for the docker container comprises the following steps: collecting fault data, and performing feature selection and feature extraction on the fault data to obtain fault data with common features; carrying out data preprocessing on the fault data with the common characteristics; constructing a fault analysis model according to fault data subjected to data preprocessing and a machine learning algorithm; collecting fault information of the docker container, and analyzing the fault information of the docker container through the fault analysis model to obtain a corresponding fault type; and carrying out corresponding fault processing according to the fault type. Therefore, according to the fault repairing method of the docker container, provided by the application, fault data are collected, the fault analysis model is built, and then automatic identification and processing of container faults are achieved through the fault analysis model without manual fault positioning and processing, so that the workload of personnel is greatly reduced, and traveling, development and testing costs are saved.
The fault repairing device, the equipment and the computer readable storage medium of the docker container have the technical effects.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the prior art and the embodiments are briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for repairing a fault of a docker container according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a fault repairing apparatus for a docker container according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of a fault repairing apparatus for a docker container according to an embodiment of the present disclosure.
Detailed Description
The core of the application is to provide a fault repairing method for a docker container, which can realize automatic positioning and processing of container faults, reduce the workload of personnel and save the cost. Another core of the present application is to provide a fault repairing apparatus, a device and a computer readable storage medium for a docker container, all having the above technical effects.
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for repairing a fault of a docker container according to an embodiment of the present disclosure, and referring to fig. 1, the method includes:
s101: collecting fault data, and performing feature selection and feature extraction on the fault data to obtain fault data with common features;
specifically, the fault analysis method and the fault analysis system realize automatic identification and processing of container faults by collecting fault data and constructing a fault analysis model. Step S101 is to collect fault data, and perform feature selection and feature extraction on the collected fault data on the basis of collecting fault data, to obtain fault data having common features. The fault data may include all error information of system downtime, operation error and abnormal function.
Additionally, in a particular embodiment, collecting fault data includes: fault data is collected from an error log of a device or system. Specifically, when a fault such as a downtime occurs in the equipment or the system, the corresponding error information is recorded in the error log, so that the fault data can be directly acquired from the error log of the equipment or the system.
The characteristic selection and the characteristic extraction of the fault information mean that fault data with common characteristics are obtained after various fault redundant information is removed. Feature selection is a process of automatically selecting a subset of features that are most important to a problem, and feature extraction is a process of automatically performing dimension reduction on raw data.
In one specific embodiment, the selecting and extracting the features of the fault data includes: and performing feature selection and feature extraction on the fault data by adopting any one of a filtering method, a wrapping method and an embedding method. Namely, any one of a filtering method, a wrapping method and an embedding method can be selected for feature selection and feature extraction. For the specific implementation process of selecting and extracting features by using a filtering method, a wrapping method and an embedding method, details are not repeated herein, and reference may be made to the existing related technologies. In addition, it is understood that other methods that can also realize feature selection and feature extraction can be adopted besides the filtering method, the wrapping method and the embedding method.
S102: carrying out data preprocessing on fault data with common characteristics;
specifically, on the basis of acquiring fault data and performing feature selection and feature extraction, data preprocessing is further performed on the fault data with common features obtained through feature selection and feature extraction, and a fault analysis model is subsequently established according to the preprocessed fault data.
In a specific embodiment, the preprocessing the fault data with the common characteristics includes: and carrying out data cleaning, data integration, data exchange and data specification processing on fault data with common characteristics.
Specifically, the purpose of data cleaning is to remove unreasonable data, wrong types of data and the like, and the unreasonable data is not allowed to enter a subsequent operation process. For example, when the failure data is almost integer, the individual character string type thereof is removed. Data integration refers to merging data in multiple data sources to form a unified table. When the data volume is large, the data volume can be stored in a data warehouse; when the data amount is not large, it can be stored in a file. Data transformation refers to finding a characteristic representation of data and using dimension transformation to reduce valid data. Data reduction refers to finding useful features of the expressed data that depend on the discovered objects to reduce the data model and thus reduce the amount of data as much as possible.
For the specific implementation of data cleaning, data integration, data exchange, and data specification, the present application does not need to be repeated herein, and only needs to refer to the existing related technologies.
S103: constructing a fault analysis model according to fault data subjected to data preprocessing and a machine learning algorithm;
specifically, the step aims to construct a fault analysis model, and then automatically identify the container fault through the fault analysis model. Specifically, on the basis of completing the data feature selection and extraction and data preprocessing, a fault analysis model is obtained by performing model training further according to fault data after data preprocessing and a machine learning algorithm.
S104: collecting fault information of the docker container, and analyzing the fault information of the docker container through a fault analysis model to obtain a corresponding fault type;
s105: and carrying out corresponding fault processing according to the fault type.
Specifically, after the fault analysis model is obtained, subsequently, when the docker container fails, fault information of the docker container is collected, the collected fault information of the docker container is input into the fault analysis model, and the fault information of the docker container is analyzed through the fault analysis model to obtain a corresponding fault type. And further, according to the fault type, adopting a processing mode corresponding to the fault type to process the fault. The corresponding relation between the fault type and the fault processing mode can be established in advance and stored, and when the fault type is determined, the corresponding processing mode can be inquired and corresponding fault processing can be carried out.
Further, in order to facilitate the manager to know the fault condition in time, in a specific embodiment, when the docker container has a fault, a fault alarm may be performed, such as lighting a fault indicator lamp.
In summary, the fault repairing method for the docker container provided by the present application includes: collecting fault data, and performing feature selection and feature extraction on the fault data to obtain fault data with common features; carrying out data preprocessing on the fault data with the common characteristics; constructing a fault analysis model according to fault data subjected to data preprocessing and a machine learning algorithm; collecting fault information of the docker container, and analyzing the fault information of the docker container through the fault analysis model to obtain a corresponding fault type; and carrying out corresponding fault processing according to the fault type. According to the fault repairing method, fault data are collected, a fault analysis model is built, automatic identification and processing of container faults are achieved through the fault analysis model, manual fault positioning and processing are not needed, accordingly, the workload of personnel is greatly reduced, and traveling, development and testing costs are saved.
The application also provides a fault repair device of a docker container, which can be referred to in correspondence with the method described above. Referring to fig. 2, fig. 2 is a schematic diagram of a fault repairing apparatus for a docker container according to an embodiment of the present disclosure, and referring to fig. 2, the apparatus includes:
the data acquisition module 10 is used for acquiring fault data, and performing feature selection and feature extraction on the fault data to obtain fault data with common features;
the data preprocessing module 20 is used for performing data preprocessing on the fault data with the common characteristics;
the model construction module 30 is used for constructing a fault analysis model according to the fault data subjected to data preprocessing and a machine learning algorithm;
the fault type analysis module 40 is used for collecting fault information of the docker container and analyzing the fault information of the docker container through the fault analysis model to obtain a corresponding fault type;
and the fault processing module 50 is configured to perform corresponding fault processing according to the fault type.
On the basis of the foregoing embodiment, optionally, the data preprocessing module 20 includes:
the data cleaning unit is used for carrying out data cleaning processing on the fault data;
the data integration unit is used for carrying out data integration processing on the fault data;
the data exchange unit is used for carrying out data exchange processing on the fault data;
and the data specification unit is used for carrying out data specification processing on the fault data.
On the basis of the foregoing embodiment, optionally, the data acquisition module 10 is configured to perform feature selection and feature extraction on the fault data by using any one of a filtering method, a wrapping method, and an embedding method.
On the basis of the above embodiments, optionally, the data collection module 10 is specifically configured to collect fault data from an error log of a device or a system.
On the basis of the above embodiment, optionally, the method further includes:
and the alarm module is used for carrying out fault alarm when the docker container has a fault.
The present application also provides a fault repairing apparatus for a docker container, which, as shown with reference to fig. 3, includes a memory 1 and a processor 2.
A memory 1 for storing a computer program;
a processor 2 for executing a computer program to implement the steps of:
collecting fault data, and performing feature selection and feature extraction on the fault data to obtain fault data with common features; carrying out data preprocessing on the fault data with the common characteristics; constructing a fault analysis model according to fault data subjected to data preprocessing and a machine learning algorithm; collecting fault information of the docker container, and analyzing the fault information of the docker container through the fault analysis model to obtain a corresponding fault type; and carrying out corresponding fault processing according to the fault type.
For the introduction of the device provided in the present application, please refer to the above method embodiment, which is not described herein again.
The present application further provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of:
collecting fault data, and performing feature selection and feature extraction on the fault data to obtain fault data with common features; carrying out data preprocessing on the fault data with the common characteristics; constructing a fault analysis model according to fault data subjected to data preprocessing and a machine learning algorithm; collecting fault information of the docker container, and analyzing the fault information of the docker container through the fault analysis model to obtain a corresponding fault type; and carrying out corresponding fault processing according to the fault type.
The computer-readable storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
For the introduction of the computer-readable storage medium provided in the present application, please refer to the above method embodiments, which are not described herein again.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device, the apparatus and the computer-readable storage medium disclosed by the embodiments correspond to the method disclosed by the embodiments, so that the description is simple, and the relevant points can be referred to the description of the method.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The method, apparatus, device, and computer-readable storage medium for repairing a fault of a docker container provided in the present application are described in detail above. The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
Claims (10)
1. A fault repairing method for a docker container is characterized by comprising the following steps:
collecting fault data, and performing feature selection and feature extraction on the fault data to obtain fault data with common features;
carrying out data preprocessing on the fault data with the common characteristics;
constructing a fault analysis model according to fault data subjected to data preprocessing and a machine learning algorithm;
collecting fault information of the docker container, and analyzing the fault information of the docker container through the fault analysis model to obtain a corresponding fault type;
and carrying out corresponding fault processing according to the fault type.
2. The method for fault recovery of docker containers of claim 1, wherein the data preprocessing of the fault data having the common characteristic comprises:
and carrying out data cleaning, data integration, data exchange and data protocol processing on the fault data with the common characteristics.
3. The fault repair method for a docker container according to claim 2, wherein the performing feature selection and feature extraction on the fault data comprises:
and performing feature selection and feature extraction on the fault data by adopting any one of a filtering method, a wrapping method and an embedding method.
4. The docker container fault recovery method of claim 3, wherein the collecting fault data comprises:
fault data is collected from an error log of a device or system.
5. The method of fault recovery of a docker container of claim 4, further comprising:
and when the docker container fails, performing fault alarm.
6. A device for fault recovery of a docker container, comprising:
the data acquisition module is used for acquiring fault data and performing feature selection and feature extraction on the fault data to obtain fault data with common features;
the data preprocessing module is used for preprocessing the fault data with the common characteristics;
the model building module is used for building a fault analysis model according to the fault data subjected to data preprocessing and a machine learning algorithm;
the fault type analysis module is used for acquiring fault information of the docker container and analyzing the fault information of the docker container through the fault analysis model to obtain a corresponding fault type;
and the fault processing module is used for carrying out corresponding fault processing according to the fault type.
7. The docker container fault recovery device of claim 6, wherein the data pre-processing module comprises:
the data cleaning unit is used for carrying out data cleaning processing on the fault data;
the data integration unit is used for carrying out data integration processing on the fault data;
the data exchange unit is used for carrying out data exchange processing on the fault data;
and the data specification unit is used for carrying out data specification processing on the fault data.
8. The fault repair device for a docker container of claim 7, wherein the data acquisition module is specifically configured to perform feature selection and feature extraction on the fault data using any one of a filtering method, a wrapping method, and an embedding method.
9. A fault-remediating apparatus of a docker container, comprising:
a memory for storing a computer program;
processor for implementing the steps of the method for fault recovery of a docker container according to any of claims 1 to 5 when executing said computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which, when being executed by a processor, carries out the steps of the method of fault recovery of a docker container according to any one of claims 1 to 5.
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| CN202010752683.1A CN111880981A (en) | 2020-07-30 | 2020-07-30 | Fault repairing method and related device for docker container |
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| EP4224317A4 (en) * | 2021-12-24 | 2024-04-10 | Beijing Baidu Netcom Sci & Tech Co Ltd | Method and apparatus for controlling distributed operation system, and device, medium and program product |
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| CN106383760A (en) * | 2016-09-19 | 2017-02-08 | 郑州云海信息技术有限公司 | Computer fault management method and apparatus |
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Application publication date: 20201103 |