JP6676590B2 - Layer optimization device, layer optimization method, and layer optimization program - Google Patents

Description

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

  2. Description of the Related Art Conventionally, a container image serving as a source of an application operating on an edge terminal in edge computing has been known. Distribute container images managed in the cloud etc. to various edge servers. Such a container image is obtained by installing middleware, libraries, application execution files, and the like necessary for executing an application in a certain OS (Operating System) and converting the image into an image.

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

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

  However, the conventional technology has a problem that the container image cannot be reduced in weight. For example, in the related art, the efficiency of image creation and distribution is improved by dividing a container image into a plurality of layers. However, since layers have a relationship between upper and lower layers and lower layers do not depend on upper layers. The process in the upper layer is not conscious of the lower layer. Therefore, 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 stored in the image in one layer 1 and the file A stored in the upper layer 2 is deleted, Contains the information of the file A, and the capacity of the file A is added to the image size at the final execution even though the file A is deleted.

  When the number of layers is unnecessarily increased, the image size increases. When the layers are excessively integrated, it is necessary to transfer middleware that is not originally required at the time of updating. Until now, the judgment of the processing to be integrated and the processing to be divided into one layer has depended on the experience and skills of the developer.

  In order to solve the above-described problem and achieve the object, the layer optimizing apparatus of the present invention performs each process included in an image definition file defining a container image serving as a source of an application for each category set in advance. A classifying unit that classifies processes into the same category by the classifying unit; an integrating unit that integrates processes so that the processes are classified into the same layer; And an arrangement section for arranging the categories so as to become higher layers.

  Further, the layer optimization method of the present invention is a layer optimization method executed by a layer optimization apparatus, wherein each process included in an image definition file defining a container image serving as a source of an application is set in advance. A classification step of classifying each of the categories, an integration step of integrating each processing such that the processing classified into the same category by the classification step is the same layer, and a category in which the processing is classified by the classification step, And a step of arranging so that the higher the update frequency, the higher the layer.

  In addition, the layer optimization program of the present invention includes a classification step of classifying each process included in an image definition file that defines a container image that is a source of an application according to a preset category, and the classification step. An integration step of integrating the processes so that the processes classified into the categories are on the same layer; and an arrangement in which the categories whose processes are classified by the classification step are arranged so that the higher the update frequency, the higher the layer. And causing the computer to execute the steps.

  According to the present invention, there is an effect that the image definition file can be optimized and the container image can be reduced in weight.

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 device according to the first embodiment. FIG. 3 is a diagram illustrating an example of information stored in a category information storage unit. FIG. 4 is a diagram illustrating an example of the layer optimization process. FIG. 5 is a diagram illustrating an example of a layer optimization process when a tag is set. FIG. 6 is a flowchart illustrating a process performed by the layer optimization device 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. The embodiment does not limit the layer optimization device, the layer optimization method, and the layer optimization program according to the present application.

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

[Configuration of distribution system]
First, a 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 has a layer optimizing device 10, a distribution server 20, and a plurality of edge terminals 30A and 30B, and each device is connected via a communication network 40. Note that, when the plurality of edge terminals 30A and 30B are described without particular distinction, they will be described as edge terminals 30. Further, the number of each device shown in FIG. 1 is only an example, and the present invention is not limited to this.

  When receiving the input of the image definition file, the layer optimization device 10 categorizes the processing in the image definition file mainly based on the update frequency in order to reduce the transfer capacity at the time of updating the image and the image size of the entire image. , The more frequently updated categories are arranged in higher layers. Further, the layer optimizing device 10 integrates processes belonging to 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 operating on the edge terminal 30 in edge computing. The container image is an image file containing an application execution environment, and is created by, for example, an application developer using an image definition file. Further, the container image is composed of a plurality of layers. Each layer is defined in an image definition file.

  The edge terminals 30A and 30B are devices used as edge servers in an edge computing architecture, for example, 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 (disks and network bandwidth) as compared with those in the data center. Therefore, it is necessary to reduce the size of the container image in order to make effective use of the disk, and it is necessary to maintain a container image format with high distribution efficiency in order to make effective use of the network.

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

[Configuration of Layer Optimization Device]
Next, the configuration of the layer optimization device 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 device according to the first embodiment. As shown in FIG. 2, the layer optimization device 10 includes a communication processing unit 11, an input unit 12, an output unit 13, a control unit 14, and a storage unit 15. The processing of each unit of the layer optimization device 10 will be described below. Note that 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 relating to various types of information exchanged with a connected device. For example, the communication processing unit 11 transmits the 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 and a mouse, and receives input operations of various information from a 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 by the control unit 14. The storage unit 15 particularly includes a category information storage unit 15a that is closely related to the present invention. For example, the storage unit 15 is a semiconductor memory device such as a random access memory (RAM) 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 on a category classified by the classifying unit 14b described later. Here, 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 a category information storage unit. As illustrated in FIG. 3, the category information storage unit 15a stores “category” as information for identifying a category, “tag” indicating whether a tag is set, and information for identifying a process 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 15a stores the category “category a”, the tag “none”, the process “process A, process B”, and the score “10” in association with each other. This means that the processes that are classified into “category a” and for which no tag is set are “process A” and “process B”, and are given a score of “10”.

  The control unit 14 has an internal memory for storing programs and required data defining various processing procedures and the like, and executes various processes by using these. Particularly, those closely related to the present invention include: It has a reception unit 14a, a classification unit 14b, an integration unit 14c, and an arrangement unit 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 receiving unit 14a receives an input of an image definition file. Further, the receiving unit 14a may receive the setting of the tag for the process included in the image definition file as well as the input of the image definition file. In addition, when receiving the setting of the tag, the receiving unit 14a also receives the score corresponding to the tag. In the following example, a case where the score given to the tag is “1” will be described as an example.

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

  When a tag is set, the classifying unit 14b classifies each process into categories set in advance, and further classifies according to whether or not a tag is set. That is, by assigning a tag to each process of the image definition file, the process can be classified into a category other than the above four categories, and can be reflected in the optimization of the image definition file. The classifying unit 14b stores in the category information storage unit 15a the category into which each process is classified and the presence or absence of a tag.

  The classifying unit 14b assigns a score corresponding to the category set in advance to the category in which each process is classified, and assigns a tag to the category in which the process in which the tag is set is classified. A 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 a score “10” is set for category a, a score “8” is set for category b, a score “4” is set for category c, and a 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, a score “1” is given to the category d. Note that the higher the score is set, the lower the update frequency is. 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 classifying unit 14b assigns the score “8” and further adds the score “1” corresponding to the tag to the category b ( A total score of “9” is given to “tag setting”.

  The integrating unit 14c integrates the processes so that the processes classified into the same category by the classifying unit 14b have the same layer. For example, the integration unit 14c integrates each process such that processes having the same score given by the classification unit 14b are in the same layer.

  The arranging unit 14d arranges the categories whose processes are classified by the classifying unit 14b such that the higher the update frequency, the higher the layer. For example, the arranging unit 14d arranges such that the higher the score given by the classifying unit 14b, the higher the layer.

  Here, an example of the layer optimization processing 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, it is assumed that the image definition file includes processes A to F. Then, the classification unit 14b classifies the processing A and the processing B into a category a, the processing C and the processing E into a category b, the processing D into a category c, and the processing F into a category d.

  Then, the integration unit 14c integrates the processes so that the processes classified into the same category by the classification unit 14b have 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 form the layer 1, and the processing C and the processing E classified into the category b become the layer 2. So that each process is integrated.

  Then, the arranging unit 14d arranges the categories whose processes are classified by the classifying unit 14b such that the higher the update frequency, the higher the layer. That is, in the example of FIG. 4, the arrangement unit 14d arranges, for example, such that the layer 4 corresponding to the category d having the highest update frequency is the highest layer, and then the layers 3 and 2 are arranged in that order. It is arranged so that layer 1 corresponding to the category a with the lowest update frequency is the lowest layer.

  An example of a layer optimization process when a tag is set will be described with reference to FIG. FIG. 5 is a diagram illustrating an example of a layer optimization process when a tag is set. As shown in FIG. 5, it is assumed that the image definition file includes processes A to F, and a tag is set for process C. Then, the classification unit 14b classifies the processing A and the processing B into the category a, classifies the processing C into the category b (with tag setting), classifies the processing E into the category b (without tag setting), and classifies the processing D into The process F is classified into category c, and the process F is classified 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 have the same layer. For example, in the example of FIG. 5, the integrating unit 14c integrates the respective processes so that the processes A and B classified into the category “a” become the layer 1.

  Then, the arranging unit 14d arranges the categories whose processes are classified by the classifying unit 14b such that the higher the update frequency, the higher the layer. That is, in the example of FIG. 5, the arrangement unit 14d arranges, for example, such that the layer 5 corresponding to the category d having the highest update frequency is the highest layer, and then the layer 4, the layer 3, and the layer 2, and the layers are arranged such that the layer 1 corresponding to the category a with the lowest update frequency is the lowest layer.

  In this way, the layer optimization device 10 classifies each process into a preset category and integrates each process so that the processes classified into the same category become the same layer, so that the number of layers increases unnecessarily. And reducing the image size. Further, the layer optimizing device 10 arranges such that the higher the update frequency, the higher the layer, so that only the image of the upper layer needs to be transferred when the image of the upper layer is updated, and the transfer capacity to another server is reduced. Can be reduced.

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

  As shown in FIG. 6, when the receiving unit 14a of the layer optimizing device 10 receives an input of an image definition file from a user (Yes at Step S101), the classifying unit 14b performs each process included in the image definition file in advance. (Step S102). The receiving unit 14a may receive the setting of the tag for the process included in the image definition file, together with the input of the image definition file. When a tag is set, the classifying unit 14b classifies each process into categories set in advance, and further classifies according to whether or not a tag is set.

  Then, the classification unit 14b calculates a score for each category (Step S103). For example, the classifying unit 14b gives a score corresponding to the category to the category, and further gives a score corresponding to the tag to the category in which the process in which the tag is set is classified.

  Then, the integrating unit 14c integrates the processes so that the processes classified into the same category by the classifying unit 14b have the same layer (Step S104). Then, the arranging unit 14d arranges such that the higher the score, the lower the layer in the category (step S105).

[Effects of the First Embodiment]
As described above, the layer optimization device 10 according to the first embodiment classifies each process included in an image definition file that defines a container image that is a source of an application, according to a preset category. Then, the layer optimizing device 10 integrates the processes so that the processes classified into the same category become the same layer. Then, the layer optimizing device 10 arranges the categories into which the processes are classified, so that the higher the update frequency, the higher the 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 device 10 unnecessarily increases the number of layers by classifying each process into a preset category and integrating the processes so that the processes classified into the same category become the same layer. Can be prevented, and the image size can be reduced. Further, the layer optimizing device 10 arranges such that the higher the update frequency, the higher the layer, so that only the image of the upper layer needs to be transferred when the image of the upper layer is updated, and the transfer capacity to another server is reduced. Can be reduced.

  In this way, the layer optimization device 10 can automatically optimize both the image size and the transfer capacity to another server when updating the image by optimizing the image definition file. I made it. Therefore, optimization that has been performed based on deep knowledge of the application developer about the container image configuration can be automatically performed.

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

  Further, among the processes described in the present embodiment, all or a part of the processes described as being performed automatically can be manually performed, or the processes described as being performed manually can be performed. Can be automatically or entirely performed by a known method. In addition, the processing procedures, control procedures, specific names, and information including various data and parameters shown in the above documents 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 has, for example, a memory 1010 and a CPU 1020. The computer 1000 has 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 components are connected by a bus 1080.

  The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM 1012. The ROM 1011 stores, for example, a boot program such as a 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, for example, a mouse 1051 and a keyboard 1052. The video adapter 1060 is connected to, for example, the display 1061.

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

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

  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 from another computer by the CPU 1020 via the network interface 1070.

Reference Signs List 10 layer optimization device 11 communication processing unit 12 input unit 13 output unit 14 control unit 14a reception unit 14b classification unit 14c integration unit 14d arrangement unit 15 storage unit 15a category information storage unit 20 distribution server 30A, 30B edge terminal

Claims (4)

A receiving unit that receives input of an image definition file that defines a container image that is a source of the application, and that receives a setting of a tag for a process included in the image definition file;
Each processing included in the image definition file, and categorized by a preconfigured category, further comprising: a classifying unit for classifying in accordance with the presence or absence of setting of the tag,
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;
An arranging unit that arranges the categories whose processes are classified by the categorizing unit, so that the higher the update frequency, the higher the layer .
A layer optimization device, comprising: The classifying unit assigns a score corresponding to a category set in advance to the category in which each process is classified, and assigns the tag to the category in which the process in which the tag is set is classified. The score according to is further given,
The layer optimizing device according to claim 1 , wherein the arranging unit arranges such that the higher the score given by the classifying unit, the higher the layer. A layer optimization method executed by a layer optimization device,
A step of receiving an input of an image definition file defining a container image serving as a source of the application, and receiving a setting of a tag for a process included in the image definition file;
Each processing included in the image definition file, and categorized by a preconfigured category, further classification step of classifying in accordance with the presence or absence of setting of the tag,
An integration step of integrating each processing so that the processing classified into the same category by the classification step is the same layer;
And arranging the categories whose processing has been classified in the classification step so that the higher the update frequency, the higher the layer. Receiving input of an image definition file that defines a container image that is a source of the application, and receiving a tag setting for a process included in the image definition file;
Each processing included in the image definition file, a classification step classifies each preconfigured category, further be classified according to the presence or absence of setting of the tag,
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;
And an arrangement step of arranging the categories whose processes are classified by the classification step so that the higher the update frequency, the higher the layer.

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