CN111694737A - Container influence measuring method and device based on mirror image dependency network - Google Patents
Container influence measuring method and device based on mirror image dependency network Download PDFInfo
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Abstract
The application relates to a container influence measuring method and device based on a mirror image dependency network. The method comprises the following steps: acquiring container mirror image data sets from an open source community and a mirror image hosting community; the container mirror dataset includes: container mirror image data; extracting a basic mirror image configuration instruction of a container configuration file in container mirror image data, and constructing a mirror image dependent network according to the basic mirror image configuration instruction; extracting static network characteristics in the mirror image dependent network; extracting time slice information in the mirror image dependent network according to the timestamp information of the container mirror image data, and extracting dynamic network characteristics in the mirror image dependent network according to the time slice information; and measuring the influence of the container according to the static network characteristics and the dynamic network characteristics. By adopting the method, the influence of the container in the image hosting community can be measured.
Description
Technical Field
The application relates to the technical field of computers, in particular to a container influence measuring method and device based on a mirror image dependence network.
Background
In recent years, the Docker container technology has attracted extensive attention in the industry, and there is sufficient evidence that the container technology can greatly improve software deployment efficiency. Docker is an OSS project for implementing operating system level virtualization, and is a technical implementation based on operating system research, mainly used for developers to create and release container Containers. Using containers, applications can share the same operating system, libraries, and binaries. The contents of the container are defined by instructions in the configuration file Dockerfile, including the specific Docker commands and the execution order in which they create the mirror Image. Docker starts a container from Image, which is a series of data layers topped by a Base Image, the Base Image. When a developer changes a container, Docker does not directly write the changes into the mirror image of the container, but adds an additional layer containing the changes to the mirror image. Since the production environment copy can be easily made in the local computer, developers can test their changes in a few seconds, so that containers can be changed quickly. Over a few million Docker container images have been hosted in the mirror hosting Community Docker Hub at present, but how to accurately identify influential containers remains a challenge.
At present, in a mirror image hosting community like a Docker Hub, related container searching mainly depends on personal experience of a developer, a related container influence measuring method is lacked, the developer cannot accurately sort container mirrors according to container influences, and further serious influence is brought to identification of influential containers. In addition, because container mirroring has complex and diverse dependency relationships, how to effectively measure the influence of developing containers in a community faces a huge challenge.
Disclosure of Invention
Therefore, in order to solve the technical problems, a container influence strength method and a container influence strength device based on a mirror image dependency network are needed to solve the problem of difficult container influence strength in a mirror image hosting community.
A container influence measurement method based on a mirror image dependency network, the method comprising:
acquiring container mirror image data sets from an open source community and a mirror image hosting community; the container mirror dataset comprises: container mirror image data;
extracting a basic mirror image configuration instruction of a container configuration file in the container mirror image data, and constructing a mirror image dependent network according to the basic mirror image configuration instruction;
extracting static network features in the mirror image dependent network;
extracting time slice information in the mirror image dependency network according to the timestamp information of the container mirror image data, and extracting dynamic network features in the mirror image dependency network according to the time slice information;
and measuring the influence of the container according to the static network characteristics and the dynamic network characteristics.
In one embodiment, the method further comprises the following steps: acquiring basic information corresponding to an open source project from an open source community, and constructing an alternative project set according to the basic information; the basic information includes: item ID, item name, and creator; screening the open-source projects subjected to mirror image hosting in the candidate project set by using a preset API of a mirror image hosting community according to the project names in the candidate project set; constructing a container project set according to the open source projects managed by the mirror images; and extracting container mirror image data aiming at each open source item in the container item set, and constructing a container mirror image data set according to the container mirror image data.
In one embodiment, the method further comprises the following steps: extracting a basic mirror image configuration instruction of a container configuration file in the container mirror image data; extracting a related container corresponding to the current container according to the basic mirror image configuration instruction; and constructing a directed node pair according to the current container and the associated container, traversing the container mirror image data set, and constructing a mirror image dependent network.
In one embodiment, the method further comprises the following steps: and deleting the repeated container mirror image data in the container mirror image data set.
In one embodiment, the static network features include: the number of nodes, the number of edges, the average degree, the average clustering coefficient, the average shortest path and the network diameter; further comprising: generating a sub-dependent network of each node in the mirror image dependent network according to a preset dependent path; and extracting the number of nodes, the number of edges, the average degree, the average clustering coefficient, the average shortest path and the network diameter of each child node according to the child dependence network.
In one embodiment, the method further comprises the following steps: extracting time slice information in the mirror image dependency network according to the timestamp information of the container mirror image data; the time slice information includes: node pair information; and extracting dynamic network characteristics in the mirror image dependent network according to the overlapping characteristics of node pair information in the time slices corresponding to the two continuous times.
In one embodiment, the method further comprises the following steps: acquiring the network influence of the current container in the mirror image dependence network; respectively carrying out weighted calculation on the network influence, the static network characteristics and the dynamic network characteristics to obtain an influence score of the current container; and measuring the influence of the current container according to the ranking of the influence scores.
A container impact metric apparatus based on a mirror image dependency network, the apparatus comprising:
the data extraction module is used for acquiring container mirror image data sets from the open source community and the mirror image hosting community; the container mirror dataset comprises: container mirror image data;
the network construction module is used for extracting a basic mirror image configuration instruction of a container configuration file in the container mirror image data and constructing a mirror image dependent network according to the basic mirror image configuration instruction;
the static characteristic extraction module is used for extracting static network characteristics in the mirror image dependent network;
the dynamic feature extraction module is used for extracting time slice information in the mirror image dependency network according to the timestamp information of the container mirror image data and extracting dynamic network features in the mirror image dependency network according to the time slice information;
and the influence measurement module is used for measuring the influence of the container according to the static network characteristics and the dynamic network characteristics.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring container mirror image data sets from an open source community and a mirror image hosting community; the container mirror dataset comprises: container mirror image data;
extracting a basic mirror image configuration instruction of a container configuration file in the container mirror image data, and constructing a mirror image dependent network according to the basic mirror image configuration instruction;
extracting static network features in the mirror image dependent network;
extracting time slice information in the mirror image dependency network according to the timestamp information of the container mirror image data, and extracting dynamic network features in the mirror image dependency network according to the time slice information;
and measuring the influence of the container according to the static network characteristics and the dynamic network characteristics.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring container mirror image data sets from an open source community and a mirror image hosting community; the container mirror dataset comprises: container mirror image data;
extracting a basic mirror image configuration instruction of a container configuration file in the container mirror image data, and constructing a mirror image dependent network according to the basic mirror image configuration instruction;
extracting static network features in the mirror image dependent network;
extracting time slice information in the mirror image dependency network according to the timestamp information of the container mirror image data, and extracting dynamic network features in the mirror image dependency network according to the time slice information;
and measuring the influence of the container according to the static network characteristics and the dynamic network characteristics.
According to the container influence measuring method and device based on the mirror image dependency network, the association between the containers is determined by extracting the mirror image data set and according to the basic mirror image configuration instruction of the mirror image data in the mirror image data set, so that the mirror image dependency network is constructed, the static network characteristics in the mirror image dependency network are extracted for each node in the mirror image dependency network, the dynamic characteristics in the mirror image dependency network are extracted according to the timestamp information, and the influence of the containers is measured according to the static network characteristics and the dynamic network characteristics. By the method, the complex dependency relationship among the containers can be analyzed, so that the efficiency of measuring the influence of the containers is improved.
Drawings
FIG. 1 is a flowchart illustrating a container influence metric method based on a mirror dependency network in an embodiment;
FIG. 2 is a block diagram of a container impact metric apparatus based on a mirror dependency network in one embodiment;
FIG. 3 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a container influence metric method based on a mirror dependency network, including the following steps:
The container mirror dataset includes: the container mirrors the data.
Many open source projects are disclosed in the open source community, and the open source projects can be extracted from the open source community, for example: ghtorent generally uses a large number of containers in an open source project, and can obtain an identifier of the open source project using container by analyzing the open source project, so that according to the identifier, container mirroring data can be extracted through an Application Programming Interface (API) provided by a mirroring hosting community, thereby obtaining a container mirroring data set.
And 104, extracting a basic mirror image configuration instruction of the container configuration file in the container mirror image data, and constructing a mirror image dependent network according to the basic mirror image configuration instruction.
In general, there are dependencies between containers, such as: the container a needs to depend on the container B, the dependency relationship can be obtained through the basic mirror image configuration instruction, and specifically, the dependent container B can be obtained through the From instruction obtained through the basic mirror image configuration instruction.
After the dependency relationship is obtained, a container can be regarded as a node, so that a mirror image dependency network can be constructed according to the dependency relationship.
And 106, extracting static network characteristics in the image dependent network.
Static network characteristics refer to characteristics acquired under static conditions, such as: the number of nodes, the number of edges, etc. The importance degree of the node can be basically reflected by the static network characteristics, such as: if the number of edges of the current node is large, the importance of the current node is high, and the influence is large.
And 108, extracting time slice information in the mirror image dependent network according to the time stamp information of the container mirror image data, and extracting dynamic network characteristics in the mirror image dependent network according to the time slice information.
Time slicing information refers to obtaining information of the image-dependent network at a certain time, since nodes in the image-dependent network change as time progresses, and the changes can be determined by timestamp information.
In general, mirroring of dynamic network characteristics against neighboring time points depends on network acquisition, for example: the change of the neighbor node of the current node is small along with the change of time, which shows that the more important the current node is, and the larger the influence is.
And step 110, measuring the influence of the container according to the static network characteristics and the dynamic network characteristics.
The network features acquired in each dimension can be used to measure the influence of the container, and specifically, each feature can be superimposed or weighted, and the like.
In the container influence strength measuring method based on the mirror image dependency network, the association between the containers is determined by extracting the mirror image data set and according to the basic mirror image configuration instruction of the mirror image data in the mirror image data set, so that the mirror image dependency network is constructed, the static network characteristics in the mirror image dependency network are extracted for each node in the mirror image dependency network, and the dynamic characteristics in the mirror image dependency network are extracted according to the timestamp information, so that the influence of the containers is measured according to the static network characteristics and the dynamic network characteristics. By the method, the complex dependency relationship among the containers can be analyzed, so that the efficiency of measuring the influence of the containers is improved.
In one embodiment, the specific step of obtaining the container mirror dataset includes: acquiring basic information corresponding to the open source project from the open source community, and constructing an alternative project set according to the basic information; the basic information includes: item ID, item name, and creator; screening open-source projects subjected to mirror image hosting in the candidate project set by using a preset API of a mirror image hosting community according to the project names in the candidate project set; constructing a container project set according to the open source projects managed by the mirror images; and extracting container mirror image data aiming at each open source item in the container item set, and constructing a container mirror image data set according to the container mirror image data.
Specifically, the open source community may be GHTorrent,https://ghtorrent.orgthe image hosting community may be a Docker Hub, and the container image data may be extracted through an API of the Docker Hub, and the container image data may be an image item ID, an image Tag time, Dockerfile content, and the like.
In one embodiment, the step of constructing the image dependent network comprises: extracting a basic mirror image configuration instruction of a container configuration file in container mirror image data, extracting an associated container corresponding to a current container according to the basic mirror image configuration instruction, constructing a directed node pair according to the current container and the associated container, traversing a container mirror image data set, and constructing a mirror image dependency network. In this embodiment, a mirror image dependency network is constructed according to the association relationship between containers.
Specifically, after the container mirror data set is acquired, duplicate container mirror data in the container mirror data set may be deleted.
In addition, the Dockerfile instruction information of the current container X is analyzed, and a basic mirror image configuration instruction, namely an associated container name Y declared by the FROM instruction, is extracted. Specifically, name/name (version) tuples are utilized to extract name and version number information of the associated container, and a directed node pair (X, Y) is constructed according to the current container X and the associated mirror image Y to represent that the container X depends on the container Y; scanning all containers in the mirror data set to form a mirror dependency network G ═ V, E }, wherein V represents a container node combination and E represents a dependent edge combination.
In one embodiment, the static network features include: the method specifically comprises the following steps of acquiring the static network characteristics, namely the number of nodes, the number of edges, the average degree, the average clustering coefficient, the average shortest path and the network diameter: and according to the sub-dependency network, extracting the number of nodes, the number of edges, the average degree, the average clustering coefficient, the average shortest path and the network diameter of each sub-node.
Specifically, the dependent path may be set according to a requirement, for example, the dependent path is set to 3, then a sub-dependent network of the current node is constructed, and then based on the sub-dependent network, the step of calculating the static network characteristic is as follows:
the Average degree (Ad) is calculated as follows:
wherein N represents the total number of nodes in the sub-dependent network, Degree (V)i) Representing the out-degree or in-degree of the current node.
The Average clustering coefficient (Acc) is calculated as follows:
where K represents the number of neighbor nodes of the current node.
The Average shortest path (Asp) is calculated as follows:
where d (.) represents the distance of two nodes.
The method for calculating the Network diameter (Nd) comprises the following steps:
Nd＝Longest(d(Vi,Vj)min)
in addition, the number of nodes and the number of edges can be directly extracted from the sub-dependency network, and are not described herein again.
In another embodiment, the step of extracting dynamic network features comprises: extracting time slice information in a mirror image dependence network according to the timestamp information of the container mirror image data; the time slice information includes: node pair information; and extracting dynamic network characteristics in the mirror image dependent network according to the overlapping characteristics of node pair information in the time slices corresponding to the two continuous times.
Specifically, the overlap feature is calculated as follows:
wherein, PtRepresenting a set of node pairs, R, active in time-slice information at time ttHas a value in the range of 0 to 1, Rt1 means that all active container node pairs remain active for the slice information at the next time t +1, while Rt0 means that the container node pair no longer exists at the next time slice. Thus, the overlapping characteristic of the container is AR ═ avg (r).
In one embodiment, the step of performing an influence metric comprises: acquiring the network influence of the current container in the mirror image dependence network, respectively carrying out weighted calculation on the network influence, the static network characteristics and the dynamic network characteristics to obtain the influence score of the current container, and measuring the influence of the current container according to the sequence of the influence scores.
Specifically, for the current container X, a PageRank algorithm is used to calculate the network influence of X in the whole container mirror image dependency network, and the calculation method is as follows:
wherein PRScore is the network influence of the container X in the ith iteration, N is the total number of containers, KXRepresenting all container sets, L, that depend on container XYRepresenting all other container sets on which container Y depends.
For each container X, calculating the network characteristic index value and evolution characteristic index value of each container X sub-network, namely the node number V (X), the edge number E (X), the average degree Ad (X), the average clustering coefficient Acc (X), the average shortest path Asp (X), the network diameter Nd (X) and the average slice similarity AR (X), and normalizing all index values in order to ensure that all index values have the same numerical dimension.
Different coefficients are respectively set for the network influence, the network characteristic index and the evolution characteristic index of the container X, and three dimensional indexes are integrated to form a container comprehensive influence magnitude model which can be used for measuring the comprehensive influence of a container mirror image in a development community, wherein the calculation method comprises the following steps:
all containers in the container mirror dataset may be sorted according to the composite influence value of container X, thereby identifying the influential container for Top-N.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 2, there is provided a container influence metric device based on a mirror dependency network, including: a data extraction module 202, a network construction module 204, a static feature extraction module 206, a dynamic feature extraction module 208, and an influence metric module 210, wherein:
the data extraction module 202 is used for acquiring container mirror image data sets from the open source community and the mirror image hosting community; the container mirror dataset comprises: container mirror image data;
the network construction module 204 is configured to extract a basic mirror configuration instruction of a container configuration file in the container mirror data, and construct a mirror image dependent network according to the basic mirror configuration instruction;
a static feature extraction module 206, configured to extract a static network feature in the image dependent network;
a dynamic feature extraction module 208, configured to extract time slice information in the mirror image dependent network according to the timestamp information of the container mirror image data, and extract a dynamic network feature in the mirror image dependent network according to the time slice information;
and an influence measurement module 210, configured to measure influence of the container according to the static network characteristic and the dynamic network characteristic.
In one embodiment, the data extraction module 202 is further configured to obtain basic information corresponding to the open-source project from the open-source community, and construct an alternative project set according to the basic information; the basic information includes: item ID, item name, and creator; screening the open-source projects subjected to mirror image hosting in the candidate project set by using a preset API of a mirror image hosting community according to the project names in the candidate project set; constructing a container project set according to the open source projects managed by the mirror images; and extracting container mirror image data aiming at each open source item in the container item set, and constructing a container mirror image data set according to the container mirror image data.
In one embodiment, the network construction module 204 is further configured to extract a base image configuration instruction of a container configuration file in the container image data; extracting a related container corresponding to the current container according to the basic mirror image configuration instruction; and constructing a directed node pair according to the current container and the associated container, traversing the container mirror image data set, and constructing a mirror image dependent network.
In one embodiment, the network construction module 204 is further configured to delete duplicate container mirror data in the container mirror data set.
In one embodiment, the static network features include: the static feature extraction module 206 is further configured to generate a sub-dependency network of each node in the mirror image dependency network according to a preset dependency path; and extracting the number of nodes, the number of edges, the average degree, the average clustering coefficient, the average shortest path and the network diameter of each child node according to the child dependence network.
In one embodiment, the dynamic feature extraction module 208 is further configured to extract time slice information in the mirror-dependent network according to the timestamp information of the container mirror data; the time slice information includes: node pair information; and extracting dynamic network characteristics in the mirror image dependent network according to the overlapping characteristics of node pair information in the time slices corresponding to the two continuous times.
In one embodiment, the influence metric module 210 is further configured to obtain a network influence of the current container in the mirror dependency network; respectively carrying out weighted calculation on the network influence, the static network characteristics and the dynamic network characteristics to obtain an influence score of the current container; and measuring the influence of the current container according to the ranking of the influence scores.
For specific limitations of the container influence metric device based on the mirror image dependency network, reference may be made to the above limitations of the container influence metric method based on the mirror image dependency network, and details are not repeated here. The modules in the container impact strength device based on the image dependency network can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a container impact strength measurement method based on a mirror dependency network. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method in the above embodiments when the processor executes the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method in the above-mentioned embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A container influence measurement method based on a mirror image dependency network, the method comprising:
acquiring container mirror image data sets from an open source community and a mirror image hosting community; the container mirror dataset comprises: container mirror image data;
extracting a basic mirror image configuration instruction of a container configuration file in the container mirror image data, and constructing a mirror image dependent network according to the basic mirror image configuration instruction;
extracting static network features in the mirror image dependent network;
extracting time slice information in the mirror image dependency network according to the timestamp information of the container mirror image data, and extracting dynamic network features in the mirror image dependency network according to the time slice information;
and measuring the influence of the container according to the static network characteristics and the dynamic network characteristics.
2. The method of claim 1, wherein obtaining container mirror datasets from an open source community and a mirror hosting community comprises:
acquiring basic information corresponding to an open source project from an open source community, and constructing an alternative project set according to the basic information; the basic information includes: item ID, item name, and creator;
screening the open-source projects subjected to mirror image hosting in the candidate project set by using a preset API of a mirror image hosting community according to the project names in the candidate project set;
constructing a container project set according to the open source projects managed by the mirror images;
and extracting container mirror image data aiming at each open source item in the container item set, and constructing a container mirror image data set according to the container mirror image data.
3. The method according to claim 1, wherein extracting a basic image configuration instruction of a container configuration file in the container image data, and constructing an image dependent network according to the basic image configuration instruction comprises:
extracting a basic mirror image configuration instruction of a container configuration file in the container mirror image data;
extracting a related container corresponding to the current container according to the basic mirror image configuration instruction;
and constructing a directed node pair according to the current container and the associated container, traversing the container mirror image data set, and constructing a mirror image dependent network.
4. The method of claim 3, further comprising, before extracting the base image configuration instructions of the container configuration file in the container data:
and deleting the repeated container mirror image data in the container mirror image data set.
5. The method of any of claims 1 to 4, wherein the static network characteristics comprise: the number of nodes, the number of edges, the average degree, the average clustering coefficient, the average shortest path and the network diameter;
extracting static network features in the image-dependent network, including:
generating a sub-dependent network of each node in the mirror image dependent network according to a preset dependent path;
and extracting the number of nodes, the number of edges, the average degree, the average clustering coefficient, the average shortest path and the network diameter of each child node according to the child dependence network.
6. The method according to any one of claims 1 to 4, wherein extracting time slice information in the mirror-dependent network according to the time stamp information of the container mirror data, and extracting dynamic network features in the mirror-dependent network according to the time slice information comprises:
extracting time slice information in the mirror image dependency network according to the timestamp information of the container mirror image data; the time slice information includes: node pair information;
and extracting dynamic network characteristics in the mirror image dependent network according to the overlapping characteristics of node pair information in the time slices corresponding to the two continuous times.
7. The method of any of claims 1 to 4, wherein measuring the impact of the container based on the static network characteristics and the dynamic network characteristics comprises:
acquiring the network influence of the current container in the mirror image dependence network;
respectively carrying out weighted calculation on the network influence, the static network characteristics and the dynamic network characteristics to obtain an influence score of the current container;
and measuring the influence of the current container according to the ranking of the influence scores.
8. A container influence strength device based on a mirror image dependency network is characterized by comprising:
the data extraction module is used for acquiring container mirror image data sets from the open source community and the mirror image hosting community; the container mirror dataset comprises: container mirror image data;
the network construction module is used for extracting a basic mirror image configuration instruction of a container configuration file in the container mirror image data and constructing a mirror image dependent network according to the basic mirror image configuration instruction;
the static characteristic extraction module is used for extracting static network characteristics in the mirror image dependent network;
the dynamic feature extraction module is used for extracting time slice information in the mirror image dependency network according to the timestamp information of the container mirror image data and extracting dynamic network features in the mirror image dependency network according to the time slice information;
and the influence measurement module is used for measuring the influence of the container according to the static network characteristics and the dynamic network characteristics.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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