CN107809467B - Method for deleting container mirror image data in cloud environment - Google Patents
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Abstract
The invention discloses a method for deleting container mirror image data in a cloud environment, which aims at solving the problems that in the practice of container technology, due to the fact that the space occupation of a mirror image disk is overlarge, a disk and network I/O (input/output) overhead is generated in the releasing process, the deployment cost is increased, and the use flexibility of a mirror image is limited. The method is applicable to two scenarios: local storage and mirror export. When the local storage is carried out, the disk storage overhead during the mirror image storage is reduced by increasing the multiplexing rate of the local basic mirror image; when the mirror image is exported, a file export model is established through files accessed in the running process of the dynamic collection container, and the exported mirror image is constructed as required, so that the size of the exported mirror image is reduced, and the functional completeness of the exported mirror image is ensured.
Description
Technical Field
The invention relates to the technical field of cloud computing containers, in particular to a method for deleting container mirror image data in a cloud environment, and more particularly relates to a mirror image size optimization method adopted when Docker container mirror images are locally stored and exported.
Background
The container technology is a running environment isolation technology similar to a sandbox mechanism, and a user can create a running operating system in a container to realize virtualization of an operating system level. Compared with the traditional virtual machine, the container technology realizes the light-weight application operation isolation in a mode of sharing kernel resources. Docker is an implementation form of a container technology and has the characteristics of high portability and development, operation and maintenance integrity.
Today, with the increasing expansion of cloud computing and big data size, the demand of enterprises for continuous integration and efficient release of products is increasing day by day. In a traditional virtualization system taking a virtual machine as a core, although isolation of applications and services can be achieved, hardware resources which are completely exclusive need to be provided, so that resource overhead of the whole system is huge. Docker, as a lightweight virtualization concept, can reduce the expenditure of resources and time relative to a virtual machine, so that the packaging, issuing and coordination of applications and services are more flexible and rapid.
The mirror in Docker is made up of a series of mirror layers, each layer containing only the incremental portion of the previous layer, forming a stacked structure. After a container is successfully created, a container layer which can be read and written is created on the original mirror layer. Operations on the container, such as creating and deleting files, are finished in the readable and writable container layer, and the read-only mirror image layer is not affected. Multiple containers can share the data of the same mirror image and simultaneously save the data state information of the containers. The storage of the container image file adopts a layered storage form. Therefore, the same part of the mirror image can be reused, the expenditure of disk space is saved, the cost of the container mirror image in network transmission is reduced, but the container operation is complicated. For the mirror image of Docker, the main structure and content are respectively packaged in different layers, so that the content of the same layer can be shared, and the purpose of saving storage space is achieved.
In practice, the mirror image contains the whole operation environment, so that the volume of the mirror image is often very large, large disk and network I/O expenditure is generated in the releasing process, the use flexibility of the mirror image is limited, the mirror image is contrary to the original purpose of simple and convenient design of a container technology, and even the deployment difficulty of the whole system is increased. Therefore, it is important to delete the image packet which is too large and redundant.
Disclosure of Invention
The invention mainly aims to solve the defects in the prior art and provides a method for deleting container mirror image data in a cloud environment.
The purpose of the invention can be achieved by adopting the following technical scheme:
a method for deleting container mirror image data in a cloud environment is characterized in that the method for deleting is applicable to a local mirror image storage mode and a mirror image export mode, and comprises the following steps:
local mirror storage mode:
and T1, operating the local image analyzer, searching the storage condition of the local image, if the image is not stored locally, executing the step T2, and if the image is stored locally, executing the step T3.
And T2, at this time, only analyzing the newly imported local mirror image, and storing the size of the newly imported mirror image, the size of the basic mirror image layer and the SHA-256 digest value of the mirror image layer into the local.
And T3, if the mirror images are stored locally, checking the number of the locally stored mirror images, if the number of the locally stored mirror images exceeds 20 mirror images, checking the basic mirror images of all the mirror images by a local mirror image analyzer, selecting the mirror image with the largest proportion value as a shared basic mirror image layer by a basic mirror image layer sharing calculation method, and storing the absolute path, the size and the MD5 abstract value of the file contained in the shared basic mirror image layer to form a basic mirror image file fingerprint library.
T4, after selecting the shared basic mirror image layer, analyzing the SHA-256 abstract value of the basic mirror image layer of all the newly added mirrors in the future, comparing the SHA-256 abstract value with the SHA-256 abstract value of the shared basic mirror image layer, if the two are consistent, directly storing the images in the local without modification; if not, go to step T5.
And the T5 local storage module analyzes the newly added mirror image, obtains the MD5 abstract value of the file contained in the mirror image, compares the MD5 abstract value with the abstract value in the file fingerprint library and eliminates all repeated parts. A new image is regenerated using the selected shared base image and the remaining portion.
Mirror export mode:
r1, when the mirror needs to be exported, executing the mirror dynamic exporting method according to the need, locating the file access information table of the mirror according to the name of the mirror to be exported, if the target file access information table can be located, executing the step R3, otherwise, executing the step R2;
r2, collecting file access information in the mirror image, when a container is generated, importing a file access probe, collecting files accessed by the container in the operation process in real time, recording the files in a text form, making a file access information table, and providing a basis for exporting the mirror image in the step R3;
r3, reading the file access information table, obtaining the file access information table of the exported mirror image, establishing a mirror image exported file prediction model, further obtaining related files which are depended on when the mirror image runs, and exporting the files to form a new mirror image.
Further, the local image analyzer collects all locally stored image information, including the size of each image, the number of layers of each image, and the sharing condition of each layer between images, and calculates the disk overhead reduced by the shared basic image layer.
Further, the basic mirror image layer sharing calculation method is that after each basic mirror image sharing the local storage is calculated, the proportion of the storage overhead reduced by the sharing basic mirror image layer to the size of the total mirror image is calculated, and the basic mirror image with the largest proportion is selected as the sharing basic mirror image layer used in the local storage.
Further, the file fingerprint library contains the absolute path, size and MD5 digest values of all files contained in the selected base image layer to be shared.
Further, the contents of the file access information table include the name of the access file, MD5 digest value, size, type, absolute path, and the process of accessing the file.
Further, the file access probe collects the executed processes, related configuration files and executable programs depending on files in real time in the container running process, and writes the absolute path, size, name and MD5 digest values of the files accessed during the running process into the file access information table after the container running process is finished.
Further, the exported file prediction model is used for acquiring all files contained in the exported mirror image, and calculating the dependency of the mirror image on the file by acquiring the absolute path and the access times of each file in the file access information table, the number and the types of accessed files in each directory and the access times of a certain file, so as to obtain the exported probability value of the directory.
Compared with the prior art, the invention has the following advantages and effects:
(1) the invention can effectively reduce the disk space occupied by the mirror image when the mirror image is stored locally, and simultaneously greatly reduces the size of the exported mirror image, thereby being convenient for the publishing and the transplanting of the mirror image.
(2) The invention reduces the size of the mirror image, ensures the deleted mirror image to have better reliability and maintainability by establishing the export model, and ensures that the mirror image can still ensure good re-edibility after being packaged and released.
(3) The invention can support all file drivers of the existing Docker, and the local mirror image is firstly containerized and then exported after deletion processing, thereby being independent of specific file drivers.
(4) The data acquisition of the invention is established on the basis of communication with a Docker network, the operation of the mirror image is separated from the original code, the original code is not modified, and the stability of the original system is ensured.
Drawings
FIG. 1 is a schematic diagram of a system suitable for use with the present invention;
FIG. 2 is a flowchart illustrating a method for deleting container mirror data in a cloud environment according to the present disclosure;
FIG. 3 is a diagram illustrating the sharing effect of the basic mirror image in the local storage mode according to the present invention;
FIG. 4 is a file access probe dataflow diagram in accordance with the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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 invention.
Examples
As shown in fig. 1, the system structure diagram of the method for deleting container mirror image data in a cloud environment is shown, and the method is applied to container mirror image scale optimization in a single-machine environment:
in this environment, local mirror data pruning includes locally stored mirrors and exported mirrors. The invention aims to increase the reuse rate of a shared mirror layer between mirrors to ensure that more upper-layer mirrors reuse the same basic mirror, thereby reducing the total storage overhead of the mirrors in the local environment; under the condition of exporting the mirror image, the generated deleted mirror image only contains the files which are depended on by the mirror image function through the acquired file access information, so that the size of the exported mirror image is reduced.
In order to more clearly illustrate the application scenario of the present invention, the following detailed analysis is performed in conjunction with the system workflow diagram (fig. 2), the basic image sharing effect diagram in the local storage mode (fig. 3), and the file access probe data flow diagram (fig. 4).
As shown in fig. 2, an application scenario of the method for deleting container mirror data in a cloud environment includes a local storage mode and a mirror export mode.
The mirror image deleting method of the local storage mode specifically comprises the following steps:
t1, when the program runs, retrieving the local mirror image storage condition, if the mirror image is not stored locally, executing the step T2, and if the mirror image exists locally, executing the step T3;
t2, at this time, only analyzing the newly imported local mirror image, and storing the size of the newly imported mirror image, the size of the basic mirror image layer and the SHA-256 abstract values of each mirror image layer to the local;
t3, if the mirror images are stored locally, checking the number of the locally stored mirror images, if the number of the locally stored mirror images exceeds 20 mirror images, checking the basic mirror images of all the mirror images, calculating the proportion of the shared basic mirror images in the total size of the mirror images, selecting the mirror image layer with the largest proportion value as the shared basic mirror image layer, and storing the file names and the file MD5 abstract values contained in the mirror image layer to form a basic mirror image file fingerprint library;
t4, after selecting the shared basic mirror image layer, analyzing the SHA-256 abstract value of the basic mirror image layer of all the newly added mirrors in the future, comparing the SHA-256 abstract value with the SHA-256 abstract value of the shared basic mirror image layer, if the two are consistent, directly storing the images in the local without modification; if not, executing step T5;
t5, the local storage module analyzes the newly added mirror image, obtains the MD5 abstract value of the file contained in the mirror image, compares the MD5 abstract value with the abstract value in the file fingerprint library, and eliminates all repeated parts;
t6, regenerating a new image using the selected shared base image and the culled remainder of step T5. Therefore, the basic mirror image part of the mirror image does not occupy the local disk space additionally, and the aim of reducing the local storage overhead is fulfilled.
The image deleting method of the image export mode specifically comprises the following steps:
r1, when the mirror needs to be exported, executing the mirror dynamic exporting method according to the need, locating the file access information table of the mirror according to the name of the mirror to be exported, if the target file access information table can be located, executing the step R3, otherwise, executing the step R2;
r2, collecting file access information in the mirror image, when a container is generated, importing a file access probe, collecting files accessed by the container in the operation process in real time, recording the files in a text form, making a file access information table, and providing a basis for exporting the mirror image in the step R3;
r3, reading the file access information table, obtaining the file access information table of the exported mirror image, establishing a mirror image exported file prediction model, further obtaining related files which are depended on when the mirror image runs, and exporting the files to form a new mirror image.
The local image analyzer is a process which runs in the local machine and communicates with the Docker, and is mainly used for acquiring the size of each image, the layered number of each image and the sharing condition of each layer among the images stored in the current local machine. In the local storage mode, the effect of sharing the basic mirror image layer is achieved by the mirror image layering information acquired by the local mirror image analyzer and the basic mirror image layer sharing calculation method.
The invention discloses a shared computing method of a basic mirror image layer, which reduces the total cost of global mirror image storage by increasing the utilization rate of the basic mirror image layer to the basic mirror image. To describe this problem, the following is defined: the virtual size of the mirror (regardless of the reduced space shared by the layers) is V, then the virtual storage overhead for the local n mirrors is V
The spatial reduction rate brought by sharing the base mirror layer can be defined as formula 1:
the method comprises the steps of obtaining a base image, wherein S represents the storage size of a shared layer, L represents the sharing times of the layer, the larger the value of η is, the more remarkable the improvement effect of the sharing of the layer on the global storage overhead is, the larger the shared base image is, the larger η is, and generally, in order to ensure the universality of the base image, the image with larger occupied space is selected to be acceptable for local storage, so that the base image for local sharing can be selected according to the value of η.
Fig. 3 is a diagram showing the effect of sharing the base image achieved in the native storage mode. In the figure, each circle is a layer of mirror image, and circles starting from the leftmost circle to the shaded circle are parts of mirror image with complete functions. In fact, the layers of each mirror image vary widely in size, with the largest layer in each mirror image accounting for 67% of the total mirror image size on average. It can be seen from the figure that by sharing the same mirror image layer, the sharing ratio of the local basic mirror image layer can be increased, and the effect that a plurality of mirror images share the same basic mirror image layer is achieved, so that the size of the disk space occupied by the mirror images in the local area is reduced as a whole.
And the file fingerprint library comprises the absolute path, the size and the MD5 digest value of all files contained in the selected base image layer to be shared. The method has the function of eliminating the repeated part of the shared basic mirror image layer in the new imported mirror image, thereby reducing the disk space occupied during local storage.
The file access probe is an independent executable file, namely a container is run from the mirror image to be modified, and the probe is executed in the container and is used for capturing the process executed in the container and the accessed file in real time. After the operation of the container is finished, the probe collects the relevant files accessed in the operation process of the container, writes the files into a file access information table in a JSON format and stores the files in the shared data volume. The specific implementation is completed by the mutual communication among 4 concurrent processes and threads, namely User, Sensor, Monitor and Collector. The User is a process for sending a start or end instruction to the probe, usually runs in a host and communicates with a Sensor through unix socket; the Sensor is a probe process for capturing file access information in the container, and is mainly used for receiving reports (report) captured by the Monitor and reporting the reports to the User process; the Monitor thread is responsible for summarizing events (even) collected from the Collector thread and marshaled into reports to be submitted to the Sensor.
FIG. 4 is a data flow diagram of a file access probe, with three data flows for the overall system: the stop stream is sent by the User, the collection of the whole probe information is completed, the probe work is stopped, the Sensor receives the stop signal and then transmits the stop signal to the Monitor, and the Monitor executes the cleaning work (clear); the report stream is sent by the Monitor, the Monitor executes an event arrangement function (ProcessEven), original access information collected from the Collector is converted into a report structure body, the report structure body is transmitted back to the Sensor, and the report structure body is further transmitted to the User by the Sensor; an even stream, which is issued by a Collector, is that the Collector executes an event capture function (GetEven) during the container operation process, and collects file access original information in real time.
The file access information table is file access information generated by a probe in the container and recorded in a JSON format. Each record takes the name of the accessed file as a main key, the corresponding attributes comprise an absolute path of the file, the size of the file, an MD5 digest value of the file, the type of the file and a process number of the accessed file, and if the type of the file is symbolic link, dereferencing is tried to be carried out, and the actual path of the file is given.
And (4) exporting a file prediction model, wherein a full probability model is adopted to determine whether a certain directory is exported completely. The design basis is that under the standard design, the files in each directory are a set for realizing a certain function, and when more files in a directory are exported, it is likely that the files in the directory are the core for realizing the mirroring function, so all the files in the directory should also be exported, which is specifically described as follows: the set of dependent files needed to export an image is defined as X, with a capacity of n. Suppose that a certain file Xi,(i∈[1,n]) The impact on whether the upper directory is exported or not is equally possible, i.e. 1/n. For any directory in the file system, 0 to a number of files and subdirectories are included. Thus, the derived probability for any one directory Y can be described as:
where m is the total number of files and subdirectories under directory Y,
as the ith file or directory
The derived probability of (2). Since files within set X must be exported, for any exported file
Equation (2) can be further written as:
wherein k is the number of files under directory Y and l is the number of subdirectories. We give a threshold ε,0 ≦ ε ≦ 1, and if P (Y) > ε, then all files under the Y directory are exported.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (7)
1. A method for deleting container mirror image data in a cloud environment is characterized by comprising a local mirror image storage mode and a mirror image export mode, wherein,
the local mirror image storage mode comprises the following steps:
t1, operating a local image analyzer, retrieving the storage condition of the local image, if the local image is not stored, executing a step T2, and if the local image is stored, executing a step T3;
t2, at this time, only analyzing the newly imported local mirror image, and storing the size of the newly imported mirror image, the size of the basic mirror image layer and the SHA-256 abstract values of each mirror image layer to the local;
t3, if the mirror images are stored locally, checking the number of the locally stored mirror images, if the number of the locally stored mirror images exceeds 20 mirror images, checking the basic mirror images of all the mirror images by a local mirror image analyzer, selecting the mirror image with the largest proportion value as a shared basic mirror image layer through a basic mirror image layer sharing calculation method, and storing the absolute path, the size and the MD5 abstract value of the file contained in the mirror image layer to form a basic mirror image file fingerprint library;
t4, after selecting the shared basic mirror image layer, analyzing the SHA-256 abstract value of the basic mirror image layer of all the newly added mirrors in the future, comparing the SHA-256 abstract value with the SHA-256 abstract value of the shared basic mirror image layer, if the two are consistent, directly storing the images in the local without modification; if not, executing step T5;
the T5 and the local storage module analyze the newly added mirror image to obtain the MD5 abstract value of the file contained in the mirror image, compare the abstract value with the abstract value in the file fingerprint library, remove all repeated parts, and regenerate a new mirror image by using the selected shared basic mirror image and the rest parts;
the mirror image export mode comprises the following steps:
r1, when the mirror needs to be exported, executing the mirror dynamic exporting method according to the need, locating the file access information table of the mirror according to the name of the mirror to be exported, if the target file access information table can be located, executing the step R3, otherwise, executing the step R2;
r2, collecting file access information in the mirror image, when a container is generated, importing a file access probe, collecting files accessed by the container in the operation process in real time, recording the files in a text form, making a file access information table, and providing a basis for exporting the mirror image in the step R3;
r3, reading the file access information table, obtaining the file access information table of the exported mirror image, establishing a mirror image exported file prediction model, further obtaining related files which are depended on when the mirror image runs, and exporting the files to form a new mirror image.
2. The method according to claim 1, wherein the local image analyzer collects all locally stored image information, including the size of each image, the number of layers of each image, and the sharing condition of each layer between images, and calculates the disk overhead reduced by the shared base image layer.
3. The method according to claim 1, wherein the shared computing method of the base image layer is to compute a ratio of storage overhead reduced by the shared base image layer to a total size of the image after each base image sharing the local storage is computed, and select the base image with the largest ratio as the shared base image layer used in the local storage.
4. The method of claim 1, wherein the file fingerprint library comprises absolute paths, sizes and MD5 digest values of all files contained in the selected base image layer to be shared.
5. The method according to claim 1, wherein the contents of the file access information table include a name of an access file, a MD5 digest, a size, a type, an absolute path, and a process of accessing the file.
6. The method according to claim 1, wherein the file access probe is an executable program that collects processes executed, related configuration files, and dependent files in real time during the container operation, and writes an absolute path, a size, a name, and an MD5 digest of a file accessed during the operation into the file access information table after the container operation is finished.
7. The method according to claim 1, wherein the exported file prediction model is configured to obtain all files included in the exported image, and obtain the exported probability value of each directory by obtaining an absolute path and access times of each file in the file access information table, the number and types of files accessed in each directory, and the access times of a certain file, and calculating a dependency of the file on the mirror.
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Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109144958B (en) * | 2018-07-02 | 2021-08-03 | 广东睿江云计算股份有限公司 | File access frequency metadata collection method and device for union file system |
| CN110912955B (en) * | 2018-09-17 | 2022-04-05 | 阿里巴巴集团控股有限公司 | Container mirror image downloading and uploading method and device |
| CN109639791A (en) * | 2018-12-06 | 2019-04-16 | 广东石油化工学院 | Cloud workflow schedule method and system under a kind of container environment |
| US11182193B2 (en) * | 2019-07-02 | 2021-11-23 | International Business Machines Corporation | Optimizing image reconstruction for container registries |
| CN112306621A (en) * | 2019-07-24 | 2021-02-02 | 中兴通讯股份有限公司 | Container layered deployment method and system |
| CN113495870A (en) * | 2020-04-01 | 2021-10-12 | 北京沃东天骏信息技术有限公司 | Mirror image construction method and device, electronic equipment and storage medium |
| CN113176886A (en) * | 2021-04-29 | 2021-07-27 | 中国工商银行股份有限公司 | Mirror image file compression operation method and device |
| CN114138414B (en) * | 2021-12-02 | 2023-08-15 | 国汽大有时空科技(安庆)有限公司 | Incremental compression method and system for container mirror image |
| CN116932465B (en) * | 2023-09-15 | 2024-01-23 | 山东未来网络研究院(紫金山实验室工业互联网创新应用基地) | Mirror image file management method, system, equipment and medium |
| CN117389690B (en) * | 2023-12-08 | 2024-03-15 | 中电云计算技术有限公司 | Mirror image package construction method, device, equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102981929A (en) * | 2012-11-05 | 2013-03-20 | 曙光云计算技术有限公司 | Management method and system for disk mirror images |
| CN106227579A (en) * | 2016-07-12 | 2016-12-14 | 深圳市中润四方信息技术有限公司 | A kind of Docker container construction method and Docker manage control station |
| CN106445515A (en) * | 2016-09-18 | 2017-02-22 | 深圳市华云中盛科技有限公司 | PaaS cloud implementation method based on containers |
| CN106790483A (en) * | 2016-12-13 | 2017-05-31 | 武汉邮电科学研究院 | Hadoop group systems and fast construction method based on container technique |
| CN107105054A (en) * | 2017-05-17 | 2017-08-29 | 郑州云海信息技术有限公司 | A kind of mirror image garbage-cleaning system and method towards docker mirror images warehouse |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102294568B1 (en) * | 2015-08-19 | 2021-08-26 | 삼성에스디에스 주식회사 | Method and apparatus for security checking of image for container |
-
2017
- 2017-10-10 CN CN201710934727.0A patent/CN107809467B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102981929A (en) * | 2012-11-05 | 2013-03-20 | 曙光云计算技术有限公司 | Management method and system for disk mirror images |
| CN106227579A (en) * | 2016-07-12 | 2016-12-14 | 深圳市中润四方信息技术有限公司 | A kind of Docker container construction method and Docker manage control station |
| CN106445515A (en) * | 2016-09-18 | 2017-02-22 | 深圳市华云中盛科技有限公司 | PaaS cloud implementation method based on containers |
| CN106790483A (en) * | 2016-12-13 | 2017-05-31 | 武汉邮电科学研究院 | Hadoop group systems and fast construction method based on container technique |
| CN107105054A (en) * | 2017-05-17 | 2017-08-29 | 郑州云海信息技术有限公司 | A kind of mirror image garbage-cleaning system and method towards docker mirror images warehouse |
Non-Patent Citations (3)
| Title |
|---|
| Multi-Granularity Memory Mirroring via Binary Translation in Cloud Environments.;Zhengwei Qi et al.;《IEEE Transactions on Network and Service Management》;20140425;第11卷(第1期);全文 * |
| Performance analysis of Union and CoW File Systems with Docker.;Rajdeep Dua et al.;《2016 International Conference on Computing, Analytics and Security Trends (CAST)》;20161221;全文 * |
| 一种概率模型的Docker镜像删减策略.;周毅 等.;《小型微型计算机系统》;20180915;第39卷(第09期);1908-1913页 * |
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