JP2019028690A - Layer optimization device, layer optimization method, and layer optimization program - Google Patents

Abstract

To conduct the optimization of an image definition file for slimming a container image.SOLUTION: A layer optimization device 10 classifies each processing included in an image definition file which defines a container image that is the base of an application, into each predetermined category. Then, the layer optimization device 10 integrates each processing so that the processing classified in the same category can sit in the same layer. Then, the layer optimization device 10 places the categories which the processing is classified into, in a manner that the categories with higher update frequency sit in the upper-level layers.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a layer optimization device, a layer optimization method, and a layer optimization program.

  Conventionally, a container image that is a source of an application that operates on an edge terminal in edge computing is known. Distribute container images managed in the cloud to various edge servers. Such a container image is an image obtained by installing middleware and libraries necessary for application execution, an application execution file, and the like on a certain OS (Operating System).

  As a means for defining a container image, there is a method in which the container image is divided into layers. A layer becomes one layer for every procedure for creating an image defined in an image definition file, and there is a method of stacking these layers to form one container image (see, for example, Non-Patent Document 1). Further, for example, when transferring an image, there is a method in which transfer for each layer is possible, and when an image transferred to an edge server is changed, only a layer having a difference is transferred. On the other hand, there is a method of integrating image layer information and managing it as one large layer (see, for example, Non-Patent Document 2).

“Dockerfile reference”, [online], Docker, [searched July 18, 2017], Internet <https://docs.docker.com/engine/reference/builder/> “Squash an image's layers (-squash) Experimental Only”, [online], Docker, [Search July 18, 2017], Internet <https://docs.docker.com/engine/reference/commandline/build/ # squash-an-images-layers-squash-experimental-only>

  However, the conventional technique has a problem that the container image cannot be reduced in weight. For example, in the conventional technology, the container image is divided into multiple layers to create and distribute images more efficiently. However, there is a relationship between upper and lower layers, and lower layers do not depend on higher layers. The lower layer is not aware of the processing in the upper layer. For this reason, a file to be deleted in the upper layer may remain in the lower layer.

  For example, since the operation is independent for each layer, even if the file A is saved in the image in a certain layer 1 and the file A saved in the upper layer 2 is deleted, the layer 1 The information of file A remains, and the file A capacity is added to the image size even though the file A is deleted at the time of final execution.

  Note that when the number of layers is increased unnecessarily, the image size increases, and when the layers are excessively integrated, middleware transfer that is not necessary at the time of update becomes necessary. The determination of the process to be integrated and divided into one layer has been dependent on the experience and skills of the developer.

  In order to solve the above-described problems and achieve the object, the layer optimization apparatus of the present invention performs each process included in an image definition file that defines a container image that is a source of an application for each preset category. The update unit has a high update frequency, the integration unit that integrates the processes so that the processes classified into the same category by the classification unit are in the same layer, and the category in which the processes are classified by the classification unit It has the arrangement | positioning part arrange | positioned so that it may become a higher layer as a category.

  The layer optimization method of the present invention is a layer optimization method executed by a layer optimization device, and sets each process included in an image definition file that defines a container image that is a source of an application in advance. A classification process for classifying each category, an integration process for integrating the processes so that the processes classified into the same category by the classification process are in the same layer, and a category in which the processes are classified by the classification process, And a placement step of placing a category having a higher update frequency so as to be a higher layer.

  Further, the layer optimization program of the present invention is the same in the classification step that classifies each process included in the image definition file that defines the container image that is the source of the application for each preset category. An integration step for integrating the processes so that the processes classified into categories are in the same layer, and an arrangement in which the categories classified by the classification step are arranged so that the higher the update frequency, the higher the layer. Causing the computer to execute the steps.

  According to the present invention, it is possible to optimize the image definition file and reduce the weight of the container image.

FIG. 1 is a diagram illustrating an example of a configuration of a distribution system according to the first embodiment. FIG. 2 is a block diagram illustrating a configuration example of the layer optimization apparatus according to the first embodiment. FIG. 3 is a diagram illustrating an example of information stored in the category information storage unit. FIG. 4 is a diagram illustrating an example of the layer optimization process. FIG. 5 is a diagram for explaining an example of the layer optimization process when a tag is set. FIG. 6 is a flowchart for explaining processing by the layer optimization apparatus according to the first embodiment. FIG. 7 is a diagram illustrating a computer that executes a layer optimization program.

  Hereinafter, embodiments of a layer optimization device, a layer optimization method, and a layer optimization program according to the present application will be described in detail with reference to the drawings. It should be noted that the layer optimization device, the layer optimization method, and the layer optimization program according to the present application are not limited by this embodiment.

[First embodiment]
In the following embodiments, the configuration of the distribution system according to the first embodiment, the configuration of the layer optimization device, the processing flow in the layer optimization device will be described in order, and finally the effect of the first embodiment Will be explained.

[Configuration of distribution system]
First, the distribution system according to the first embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of a configuration of a distribution system according to the first embodiment. The distribution system according to the first embodiment includes a layer optimization device 10, a distribution server 20, and a plurality of edge terminals 30 </ b> A and 30 </ b> B, and each device is connected via a communication network 40. Note that the edge terminals 30 </ b> A and 30 </ b> B are referred to as edge terminals 30 when they are described without distinction. Moreover, the number of each apparatus shown in FIG. 1 is an example to the last, and is not restricted to this.

  Upon receiving the input of the image definition file, the layer optimization apparatus 10 categorizes the processing in the image definition file mainly based on the update frequency in order to reduce the transfer capacity when updating the image and the image size of the entire image. A category having a higher update frequency is arranged in a higher layer. Further, the layer optimization apparatus 10 integrates processes in the same category into the same layer without dividing the layer.

  The distribution server 20 distributes to the edge terminal 30 a container image that is a source of an application that runs on the edge terminal 30 in edge computing. A container image is an image file that includes an application execution environment, and is created by an application developer using an image definition file, for example. The container image is composed of a plurality of layers. Each layer is defined by an image definition file.

  The edge terminals 30A and 30B are, for example, devices used as edge servers in the edge computing architecture, and are devices that operate applications. The edge terminals 30A and 30B receive the container image distributed from the distribution server 20, and operate the application based on the received container image. In the edge architecture, the edge terminals 30A and 30B often have poor resources (disk and network bandwidth) compared to the data center. Therefore, it is necessary to reduce the container image in order to effectively use the disk, and it is necessary to maintain a container image format with good distribution efficiency in order to effectively use the network.

  The layer optimization apparatus 10 according to the first embodiment automatically optimizes the image definition file to automatically reduce both the image size and the transfer capacity to another server when updating the image. Made it possible to do. For this reason, the optimization that has been performed based on the deep knowledge of the container image configuration of the application developer can be automatically performed.

[Configuration of layer optimization device]
Next, the configuration of the layer optimization apparatus 10 shown in FIG. 1 will be described with reference to FIG. FIG. 2 is a block diagram illustrating a configuration example of the layer optimization apparatus according to the first embodiment. As illustrated in FIG. 2, the layer optimization apparatus 10 includes a communication processing unit 11, an input unit 12, an output unit 13, a control unit 14, and a storage unit 15. Hereinafter, processing of each unit included in the layer optimization apparatus 10 will be described. The distribution server 20 may hold some or all of the functions of the layer optimization device 10.

  The communication processing unit 11 controls communication related to various types of information exchanged with a connected device. For example, the communication processing unit 11 transmits a container image created based on the optimized image definition file to the distribution server 20.

  The input unit 12 is an input device such as a keyboard or a mouse, and accepts various information input operations from the user. The output unit 13 is an output device such as a display, and outputs various information.

  The storage unit 15 stores data and programs necessary for various processes performed by the control unit 14, and has a category information storage unit 15a particularly closely related to the present invention. For example, the storage unit 15 is a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk.

  The category information storage unit 15a stores information related to categories classified by the classification unit 14b described later. Here, the information stored in the category information storage unit 15a will be described with reference to FIG. FIG. 3 is a diagram illustrating an example of information stored in the category information storage unit. As illustrated in FIG. 3, the category information storage unit 15 a includes “category” that is information for identifying a category, “tag” that indicates whether a tag is set, and information that identifies processing classified into each category. And the “points” assigned to each category are stored in association with each other.

  As illustrated in FIG. 3, the category information storage unit 15 a stores a category “category a”, a tag “none”, a process “process A, process B”, and a score “10” in association with each other. This means that the processing classified into “category a” and having no tag set is “processing A” and “processing B”, and the score “10” is given.

  The control unit 14 has an internal memory for storing a program that defines various processing procedures and the necessary data, and performs various processes by using these programs. Particularly, as closely related to the present invention, It has the reception part 14a, the classification | category part 14b, the integration part 14c, and the arrangement | positioning part 14d. Here, the control unit 14 is an electronic circuit such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit), or an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

  The accepting unit 14a accepts input of an image definition file. The accepting unit 14a may accept the setting of a tag for processing included in the image definition file together with the input of the image definition file. In addition, when accepting a tag setting, the accepting unit 14a also accepts a score corresponding to the tag. In the following example, a case where the number of points assigned to a tag is “1” will be described as an example.

  The classification unit 14b classifies each process included in the image definition file that defines the container image that is the source of the application, for each preset category. For example, the classification unit 14b includes, as preset categories, a category a regarding “distribution definition”, a category b regarding “middleware package acquisition”, a category c regarding “application build”, and a category regarding “environment variable setting”. Each process is classified into one of d. Note that the conditions for classification into the above four categories may be set in any way, and detailed description thereof is omitted here.

  In addition, when a tag is set, the classification unit 14b classifies each process for each category set in advance, and further classifies according to presence / absence of tag setting. That is, by assigning a tag to each process of the image definition file, it is possible to classify into a category other than the above four categories, and it is possible to reflect the optimization in the image definition file. The classification unit 14b stores the category into which each process is classified and the presence / absence of a tag in the category information storage unit 15a.

  Further, the classification unit 14b assigns a score corresponding to a category set in advance to the category in which each process is classified, and adds a score to the category in which the process for which the tag is set is classified. The corresponding score is further given. Then, the classification unit 14b stores the assigned score in the category information storage unit 15a.

  For example, it is assumed that score “10” is set for category a, score “8” is set for category b, score “4” is set for category c, and score “1” is set for category d. In this case, the classification unit 14b assigns a score “10” to the category “a”, assigns a score “8” to the category “b”, and assigns a score “4” to the category “c”. Then, the score “1” is assigned to the category d. In addition, it means that the update frequency is low for a category set with a high score. That is, category a is a category related to the process with the lowest update frequency, and category d is a category related to the process with the highest update frequency.

  Further, for example, when the process to which the tag is assigned is classified into the category b, the classification unit 14b assigns a score “8”, and further adds a score “1” corresponding to the tag, so that the category b ( A total score of “9” is given to “with tag setting”.

  The integration unit 14c integrates the processes so that the processes classified into the same category by the classification unit 14b are in the same layer. For example, the integration unit 14c integrates each process so that processes with the same number of points assigned by the classification unit 14b are in the same layer.

  The placement unit 14d places the categories whose processing has been classified by the classification unit 14b so that the category with the higher update frequency becomes a higher layer. For example, the arranging unit 14d arranges the category having a higher score assigned by the classifying unit 14b so that it is a higher layer.

  Here, an example of the layer optimization process will be described with reference to FIG. FIG. 4 is a diagram illustrating an example of the layer optimization process. As shown in FIG. 4, the image definition file includes processing A to processing F. Then, the classification unit 14b classifies the processes A and B into the category a, classifies the processes C and E into the category b, classifies the process D into the category c, and classifies the process F into the category d.

  Then, the integration unit 14c integrates the processes so that the processes classified into the same category by the classification unit 14b are in the same layer. For example, in the example of FIG. 4, the integration unit 14c integrates the processing A and the processing B classified into the category a so as to be layer 1, and the processing C and the processing E classified into the category b become layer 2. So that each process is integrated.

  Then, the arranging unit 14d arranges the categories whose processing is classified by the classifying unit 14b so that the category having a higher update frequency becomes a higher layer. That is, in the example of FIG. 4, the placement unit 14d places, for example, the layer 4 corresponding to the category d having the highest update frequency so that it becomes the highest layer, and then, in the order of layer 3 and layer 2 Arrangement is made so that layer 1 corresponding to category a having the lowest update frequency is the lowest layer.

  An example of layer optimization processing when a tag is set will be described with reference to FIG. FIG. 5 is a diagram for explaining an example of the layer optimization process when a tag is set. As shown in FIG. 5, it is assumed that the image definition file includes process A to process F, and a tag is set for process C. Then, the classification unit 14b classifies the process A and the process B into the category a, classifies the process C into the category b (with tag setting), classifies the process E into the category b (without tag setting), and processes the process D. Classify into category c and process F into category d.

  Then, the integration unit 14c integrates the processes so that the processes classified into the same category by the classification unit 14b are in the same layer. For example, in the example of FIG. 5, the integration unit 14 c integrates each process so that the process A and the process B classified into the category a become the layer 1.

  Then, the arranging unit 14d arranges the categories whose processing is classified by the classifying unit 14b so that the category having a higher update frequency becomes a higher layer. That is, in the example of FIG. 5, the placement unit 14d places, for example, the layer 5 corresponding to the category d having the highest update frequency so as to be the highest layer, and then, the layer 4, the layer 3, the layer The layer 1 is arranged in the order of 2, and the layer 1 corresponding to the category a having the lowest update frequency is arranged to be the lowest layer.

  In this manner, the layer optimization apparatus 10 unnecessarily increases the number of layers by classifying the processes into preset categories and integrating the processes so that the processes classified in the same category become the same layer. It is possible to reduce the image size. In addition, since the layer optimization apparatus 10 is arranged so that the category with the higher update frequency becomes the higher layer, it is only necessary to transfer the image of the upper layer when updating the image of the upper layer, and the transfer capacity to another server is increased. Can be reduced.

[Processing flow of layer optimization device]
Next, a processing flow of the layer optimization apparatus 10 according to the first embodiment will be described with reference to FIG. FIG. 6 is a flowchart for explaining processing by the layer optimization apparatus according to the first embodiment.

  As illustrated in FIG. 6, when the receiving unit 14a of the layer optimization device 10 receives an input of an image definition file from the user (Yes in step S101), the classification unit 14b performs each process included in the image definition file in advance. Are classified for each category set (step S102). The receiving unit 14a may receive a tag setting for processing included in the image definition file together with the input of the image definition file. In addition, when a tag is set, the classification unit 14b classifies each process for each category set in advance, and further classifies according to presence / absence of tag setting.

  And the classification | category part 14b calculates a score for every category (step S103). For example, the classification unit 14b assigns a score corresponding to the category to the category, and further assigns a score corresponding to the tag to the category in which the process in which the tag is set is classified.

  Then, the integration unit 14c integrates the processes so that the processes classified into the same category by the classification unit 14b are in the same layer (Step S104). Then, the arranging unit 14d arranges the category having a higher score so that the lower layer becomes a lower layer (step S105).

[Effect of the first embodiment]
As described above, the layer optimization apparatus 10 according to the first embodiment classifies each process included in the image definition file that defines the container image that is the source of the application, for each preset category. And the layer optimization apparatus 10 integrates each process so that the process classified into the same category may become the same layer. And the layer optimization apparatus 10 arrange | positions the category by which the process was classified so that a category with higher update frequency may become a higher layer. For this reason, it is possible to optimize the image definition file and reduce the weight of the container image.

  That is, the layer optimization apparatus 10 unnecessarily increases the layers by classifying the processes into preset categories and integrating the processes so that the processes classified in the same category become the same layer. It is possible to reduce the image size. In addition, since the layer optimization apparatus 10 is arranged so that the category with the higher update frequency becomes the higher layer, it is only necessary to transfer the image of the upper layer when updating the image of the upper layer, and the transfer capacity to another server is increased. Can be reduced.

  As described above, the layer optimization device 10 can automatically reduce both the image size and the transfer capacity to another server when updating the image by optimizing the image definition file. I made it. For this reason, the optimization that has been performed based on the deep knowledge of the container image configuration of the application developer can be automatically performed.

[System configuration, etc.]
Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured. Furthermore, all or a part of each processing function performed in each device may be realized by a CPU and a program that is analyzed and executed by the CPU, or may be realized as hardware by wired logic.

  In addition, among the processes described in the present embodiment, all or part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed All or a part of the above can be automatically performed by a known method. In addition, the processing procedure, control procedure, specific name, and information including various data and parameters shown in the above-described document and drawings can be arbitrarily changed unless otherwise specified.

[program]
FIG. 7 is a diagram illustrating a computer that executes a layer optimization program. The computer 1000 includes a memory 1010 and a CPU 1020, for example. The computer 1000 also includes a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. These units are connected by a bus 1080.

  The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM 1012. The ROM 1011 stores a boot program such as BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to the hard disk drive 1090. The disk drive interface 1040 is connected to the disk drive 1100. For example, a removable storage medium such as a magnetic disk or an optical disk is inserted into the disk drive 1100. The serial port interface 1050 is connected to a mouse 1051 and a keyboard 1052, for example. The video adapter 1060 is connected to the display 1061, for example.

  The hard disk drive 1090 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. That is, a program that defines each process of the layer optimization apparatus 10 is implemented as a program module 1093 in which a code executable by a computer is described. The program module 1093 is stored in the hard disk drive 1090, for example. For example, a program module 1093 for executing processing similar to the functional configuration in the apparatus is stored in the hard disk drive 1090. The hard disk drive 1090 may be replaced by an SSD (Solid State Drive).

  Data used in the processing of the above-described embodiment is stored as program data 1094 in, for example, the memory 1010 or the hard disk drive 1090. Then, the CPU 1020 reads the program module 1093 and the program data 1094 stored in the memory 1010 and the hard disk drive 1090 to the RAM 1012 and executes them as necessary.

  The program module 1093 and the program data 1094 are not limited to being stored in the hard disk drive 1090, but may be stored in, for example, a removable storage medium and read out by the CPU 1020 via the disk drive 1100 or the like. Alternatively, the program module 1093 and the program data 1094 may be stored in another computer connected via a network or a WAN. Then, the program module 1093 and the program data 1094 may be read by the CPU 1020 from another computer via the network interface 1070.

DESCRIPTION OF SYMBOLS 10 Layer optimization apparatus 11 Communication processing part 12 Input part 13 Output part 14 Control part 14a Reception part 14b Classification | category part 14c Integration part 14d Arrangement | positioning part 15 Storage part 15a Category information storage part 20 Distribution server 30A, 30B Edge terminal

Claims (5)

A classification unit that classifies each process included in the image definition file that defines the container image that is the source of the application, for each preset category;
An integration unit that integrates each process so that the processes classified into the same category by the classification unit are in the same layer;
A layer optimizing device, comprising: an arrangement unit that arranges a category in which processing is classified by the classification unit so that a category having a higher update frequency becomes a higher layer. A receiving unit that receives an input of the image definition file and receives a tag setting for a process included in the image definition file;
The layer optimization apparatus according to claim 1, wherein the classification unit classifies the processes for each category set in advance, and further classifies the processes according to whether the tag is set. The classification unit assigns a score according to a category set in advance to the category in which each process is classified, and the category in which the process in which the tag is set is classified According to the score,
The layer optimizing apparatus according to claim 2, wherein the arranging unit arranges so that a category having a higher score assigned by the classifying unit becomes a higher layer. A layer optimization method executed by a layer optimization device, comprising:
A classification process that classifies each process included in the image definition file that defines the container image that is the source of the application into categories set in advance,
An integration step of integrating each processing so that the processing classified into the same category by the classification step becomes the same layer;
A layer optimizing method comprising: arranging a category in which processing is classified by the classification step so that a category having a higher update frequency becomes a higher layer. A classification step that classifies each process included in the image definition file that defines the container image that is the source of the application, according to a preset category,
An integration step of integrating the processes so that the processes classified into the same category by the classification step are in the same layer;
A layer optimization program for causing a computer to execute a placement step of placing a category in which processing is classified in the classification step so that a category having a higher update frequency becomes a higher layer.

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