CN107659653B - NDN network measurement data caching method and device, electronic equipment and storage medium - Google Patents

Abstract

Embodiments of the present invention provide a method, device, electronic device, and storage medium for NDN network measurement data caching. The method includes: acquiring basic structure metadata and functional structure metadata of each storage node of the NDN network; For each two storage nodes in the storage node, based on the functional structure metadata of the two storage nodes and the basic structure metadata of the two storage nodes, determine the association relationship existing between the two storage nodes; based on each storage node Based on the network relationship topology map, calculate the correlation degree between each storage node and other storage nodes in each storage node; based on the correlation degree, determine each storage node Whether each storage node in the node is a key network node, and caches network measurement data at the key network node. By applying the embodiments of the present invention, the caching efficiency of NDN network measurement data can be improved.

Description

NDN network measurement data caching method, device, electronic device and storage medium

technical field

The present invention relates to the field of communications, and in particular, to a method, device, electronic device and storage medium for buffering NDN network measurement data.

Background technique

At present, the increasing user scale and user demands have brought huge challenges to the Internet, making the content itself more and more the center of user demands. Based on this, NDN (Named Data Network, Named Data Network) came into being. Caching the network measurement data in the NDN network can optimize the NDN network, thereby improving the availability of the NDN network. Among them, network measurement data is mainly used to monitor the network, including monitoring of network operation, monitoring of network resources and network performance (such as service throughput, delay, packet loss rate, RTT, bandwidth utilization, network scalability, etc.). etc.) monitoring, etc., and can submit fault and abnormal event reports and make corresponding evaluations.

The existing NDN network measurement data caching method is to cache the network measurement data on each storage node. Specifically, in an NDN network, each storage node is equivalent to a server, and network measurement data is cached in each server, so as to measure the network performance data of the corresponding server through the network measurement data, so as to monitor the NDN network.

However, in the existing NDN network measurement data caching method, since the network performance data on each server is updated in real time, there will be more network performance data measured within a preset time. Network performance data to determine whether the corresponding server is available is not only computationally intensive, but also takes a long time, resulting in low NDN network measurement data caching efficiency.

SUMMARY OF THE INVENTION

The purpose of the embodiments of the present invention is to provide a NDN network measurement data caching method, apparatus, electronic device and storage medium, so as to improve the NDN network measurement data caching efficiency. The specific technical solutions are as follows:

The embodiment of the present invention discloses a NDN network measurement data caching method, the method includes:

Acquiring basic structure metadata and functional structure metadata of each storage node of the NDN network, wherein the basic structure metadata represents a static feature of the storage node, and the functional structure metadata represents a dynamic feature of the storage node;

For every two storage nodes in the storage nodes, based on the functional structure metadata of the two storage nodes and the basic structure metadata of the two storage nodes, determine the association existing between the two storage nodes relation;

based on the association relationship between the storage nodes, constructing a topology map of the network relationship between the storage nodes;

Calculate the degree of association between each storage node and other storage nodes in the storage nodes based on the network relationship topology map;

Based on the correlation degree, it is determined whether each of the storage nodes is a key network node, and network measurement data is cached at the key network node.

Optionally, after obtaining the basic structure metadata and functional structure metadata of each storage node of the NDN network, the method further includes:

The infrastructure metadata and the functional structure metadata are stored in a metadata pool.

Optionally, based on the functional structure metadata of the two storage nodes and the infrastructure metadata in the two storage nodes, determine the association relationship existing between the two storage nodes, including:

After the infrastructure metadata of one of the two storage nodes responds, the functional structure metadata of the storage node will trigger the response of the infrastructure metadata of the other storage node. There is a sequential relationship between them;

When the basic structure metadata of the two storage nodes respond at the same time, the functional structure metadata of one of the two storage nodes will trigger the occurrence of the basic structure metadata of other storage nodes except the two storage nodes. When responding, it is determined that there is a parallel relationship between the two storage nodes;

When both the basic structure metadata of the two storage nodes respond, the functional structure metadata of one of the two storage nodes will trigger the other storage nodes except the two storage nodes with a preset probability. When the infrastructure metadata responds, it is determined that there is a conditional relationship between the two storage nodes;

After the infrastructure metadata of one of the two storage nodes responds, the functional structure metadata of the storage node will trigger the infrastructure metadata of the other storage node and other storage nodes other than the two storage nodes. When the infrastructure metadata of the node responds, it is determined that there is a branch relationship between the two storage nodes.

Optionally, constructing a network relationship topology diagram between the storage nodes based on the association relationship between the storage nodes, including:

Connect the storage nodes that have at least one of the sequence relationship, the parallel relationship, the conditional relationship, and the branch relationship among the storage nodes, to obtain the relationship between the storage nodes. Network relationship topology diagram.

Optionally, calculating the degree of association between each of the storage nodes and other storage nodes based on the network relationship topology diagram, including:

Calculate, based on the network relationship topology diagram, the number of storage nodes that have an associated relationship between each storage node and other storage nodes in the storage nodes;

According to the number of the storage nodes, the degree of association between each of the storage nodes and other storage nodes is determined.

Optionally, determining whether each storage node in the storage nodes is a key network node based on the correlation degree includes:

The storage node whose correlation degree is greater than the preset threshold is used as the key network node of the NDN network.

The embodiment of the present invention also provides an NDN network measurement data cache device, the device includes:

The acquisition module is used to acquire the basic structure metadata and functional structure metadata of each storage node of the NDN network, wherein the basic structure metadata represents the static characteristics of the storage node, and the functional structure metadata represents the dynamic characteristics of the storage node ;

a first determining module, configured to, for every two storage nodes in the storage nodes, determine the two storage nodes based on the functional structure metadata of the two storage nodes and the basic structure metadata of the two storage nodes The association between storage nodes;

a building module, configured to build a topology map of the network relationship between the storage nodes based on the association relationship between the storage nodes;

a calculation module, configured to calculate the degree of association between each of the storage nodes and other storage nodes based on the network relationship topology diagram;

The second determination module is configured to determine whether each storage node in the storage nodes is a key network node based on the correlation degree, and cache network measurement data at the key network node.

Optionally, the device further includes:

A storage module, configured to store the infrastructure metadata and the functional structure metadata in a metadata pool.

Optionally, the first determining module includes:

The first determination submodule is used to trigger the response of the infrastructure metadata of another storage node through the functional structure metadata of the storage node after the infrastructure metadata of one storage node of the two storage nodes responds , it is determined that there is a sequential relationship between the two storage nodes;

The second determination sub-module is used to trigger the other storage nodes except the two storage nodes through the functional structure metadata of one storage node in the two storage nodes after the two storage nodes respond simultaneously. When the infrastructure metadata of the storage node responds, it is determined that there is a parallel relationship between the two storage nodes;

The third determination sub-module is configured to trigger the two storage nodes with a preset probability through the functional structure metadata of one of the two storage nodes after the basic structure metadata of the two storage nodes responds When the infrastructure metadata of other storage nodes other than the node responds, it is determined that there is a conditional relationship between the two storage nodes;

The fourth determination sub-module is used to trigger the basic structure metadata of the other storage node and the two storage nodes through the functional structure metadata of the storage node after the response of the basic structure metadata of one of the two storage nodes. When the infrastructure metadata of other storage nodes other than one storage node responds, it is determined that there is a branch relationship between the two storage nodes.

Optionally, the building module is specifically used for:

Connect the storage nodes that have at least one of the sequence relationship, the parallel relationship, the conditional relationship, and the branch relationship among the storage nodes, to obtain the relationship between the storage nodes. Network relationship topology diagram.

Optionally, the computing module includes:

a calculation submodule, configured to calculate, based on the network relationship topology diagram, the number of storage nodes that have an associated relationship between each storage node and other storage nodes in the storage nodes;

The fifth determination sub-module is configured to determine, according to the number of the storage nodes, the degree of association between each of the storage nodes and other storage nodes.

Optionally, the second determining module is specifically used for:

The storage node whose correlation degree is greater than the preset threshold is used as the key network node of the NDN network.

The embodiment of the present invention also discloses an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus;

the memory for storing computer programs;

The processor is configured to implement the steps of the above-mentioned NDN network measurement data caching method when executing the program stored in the memory.

In yet another aspect of the implementation of the present invention, a computer-readable storage medium is also disclosed, wherein instructions are stored in the computer-readable storage medium, and when the computer-readable storage medium is run on a computer, the computer is made to execute any one of the above-mentioned one An NDN network measurement data caching method.

In a method, device, electronic device, and storage medium for NDN network measurement data caching provided by the embodiments of the present invention, the basic structure metadata and functional structure metadata of each storage node of the NDN network are firstly obtained, and then the Two storage nodes, based on the functional structure metadata of the two storage nodes and the infrastructure metadata in the two storage nodes, determine the association relationship existing between the two storage nodes, and then based on the relationship between the storage nodes Based on the network relationship topology map, calculate the correlation degree between each storage node and other storage nodes in each storage node; finally, based on the correlation degree, determine each storage node. Whether each storage node in the node is a key network node, and caches network measurement data at the key network node. By correlating the basic structure metadata and functional structure metadata in the network data, the degree of correlation between each storage node and other storage nodes is obtained, and the key network nodes in each storage node are determined according to the degree of correlation, so that the The network measurement data is stored in the key network nodes, network monitoring is performed on the key network nodes, and the NDN network is optimized, thereby improving the caching efficiency of the NDN network measurement data. Of course, it is not necessary for any product or method to implement the present invention to simultaneously achieve all of the advantages described above.

Description of drawings

In order to illustrate the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that are used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative efforts.

1 is a first schematic flowchart of a method for caching measurement data of an NDN network provided by an embodiment of the present invention;

2 is a second schematic flowchart of a NDN network measurement data caching method according to an embodiment of the present invention;

3 is a third schematic flowchart of a method for caching NDN network measurement data according to an embodiment of the present invention;

4 is a schematic structural diagram of an NDN network measurement data buffering device according to an embodiment of the present invention;

FIG. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

With the development of mobile wireless networks and the popularization of portable mobile devices, people's demands on wireless networks are not only satisfied with end-to-end communication. Today, content-centric communication models are becoming more and more popular. Named data network originated from a branch of content-centric network. Unlike traditional IP network, which reduces communication delay and communication interruption by improving the quality of end-to-end communication links, the communication mode of named data network focuses more on how to get content information, not where to get it. Among them, each storage node in the NDN network caches the network measurement data, which can monitor the network in time, thereby improving the availability of the NDN network. However, through the existing NDN network measurement data caching method, the NDN network measurement data caching efficiency is relatively low. Therefore, in order to solve the problem of NDN network measurement data caching efficiency, the present invention proposes a NDN network measurement data caching method, which can optimize the NDN network caching strategy based on the association relationship of network storage nodes, and further improve the network measurement data caching efficiency. The specific plans are as follows:

Referring to FIG. 1, FIG. 1 is a first schematic flowchart of a NDN network measurement data caching method according to an embodiment of the present invention, including the following steps:

S101. Acquire basic structure metadata and functional structure metadata of each storage node of the NDN network, where the basic structure metadata represents static features of the storage nodes, and the functional structure metadata represents dynamic features of the storage nodes.

Specifically, in an NDN network, network data mainly comes from local detection data collected by each node deploying clients, including but not limited to network performance data, node performance data, transmission control data, node transmission content, network log information, etc. . These network data can be divided into basic metadata, technical metadata, structural metadata, preservation metadata, traceability metadata, management metadata and process metadata. Among them, the basic metadata represents the static feature description of the data itself of the storage node, the technical metadata represents the technical parameter description of the storage node, the structural metadata represents the affiliation of the storage node, and the preservation metadata represents the version feature of the basic metadata. And the description of the environment features around the version, the traceability metadata represents the context description and environment description of the recorded network data in the use process, the management metadata represents the management action or operation description in the network data, and the procedural metadata represents the network data. A description of the change information generated by the processing process.

These network data are classified in advance, and the basic metadata, technical metadata and structural metadata that can represent the static characteristics of storage nodes are used as basic structural metadata, and the dynamic characteristics of storage nodes can be represented. Preservation metadata, traceability Functional metadata, administrative metadata and procedural metadata are regarded as functional structure metadata, that is, network data is divided into basic structure metadata and functional structure metadata.

S102, for every two storage nodes in the storage nodes, based on the functional structure metadata of the two storage nodes and the basic structure metadata of the two storage nodes, determine that there is a relationship between the two storage nodes association relationship.

Specifically, whether there is an association relationship between each two storage nodes in each storage node is determined by the basic structure metadata and functional structure metadata in the two storage nodes. For example, one of the two storage nodes The infrastructure metadata occurrence response is related to another infrastructure metadata occurrence response, which may be a simultaneous response or a sequential response, wherein the response is triggered by the functional structure metadata. Here, by determining the association relationship existing between every two storage nodes, the association relationship between each storage node and other storage nodes can be obtained, so as to avoid repeatedly determining the association relationship between the storage nodes.

S103. Based on the association relationship between the storage nodes, construct a network relationship topology map between the storage nodes.

Specifically, the network relationship topology diagram includes each storage node and the edge connected to each storage node. The edge or line here represents the association relationship between the storage nodes. You can use lines between the storage nodes with the association relationship. They are connected to form edges with associated relationships, so that through these storage nodes and these edges, a network relationship topology graph is formed. Here, the relationship between each storage node is reflected by a network relationship topology diagram, so that the relationship between each storage node and other storage nodes can be obtained intuitively and conveniently.

S104, based on the network relationship topology map, calculate the degree of association between each of the storage nodes and other storage nodes.

Specifically, the association degree represents the association degree of each storage node in each storage node with other storage nodes, that is, the more other storage nodes have an association relationship with a storage node, the greater the association degree of the storage node. Therefore, in the network relationship topology diagram, by calculating the number of associations between each storage node and other storage nodes in each storage node, the degree of association between each storage node and other storage nodes in each storage node can be obtained. , or by calculating the number of edges or the number of lines that have an associated relationship between each storage node in each storage node and other storage nodes. Here, by calculating the degree of association between each storage node and other storage nodes, the importance of each storage node can be quickly determined.

S105, based on the correlation degree, determine whether each storage node in the storage nodes is a key network node, and cache network measurement data at the key network node.

Specifically, the association degree indicates the number of other storage nodes that have an association relationship with a storage node. The more other storage nodes that have an association relationship, the greater the association degree of the storage node, and the more critical network node the storage node is. Likewise, the fewer other storage nodes that have an association relationship, the smaller the association degree of the storage node is, and the storage node is a non-critical network node. After the key network nodes are determined, the network measurement data can be cached at the key network nodes, so that the network monitoring of the key network nodes can be performed through the network measurement data, and the NDN network can be optimized, thereby improving the caching efficiency of the NDN network measurement data.

It can be seen that, in a method for caching measurement data of an NDN network provided by an embodiment of the present invention, the basic structure metadata and functional structure metadata of each storage node of the NDN network are first obtained, and then each two storage nodes in each storage node are , based on the functional structure metadata of the two storage nodes and the basic structure metadata in the two storage nodes, determine the association relationship between the two storage nodes, and then based on the association relationship between the storage nodes, Build a network relationship topology map between each storage node, and based on the network relationship topology map, calculate the correlation degree between each storage node in each storage node and other storage nodes; finally, based on the correlation degree, determine each storage node in each storage node. Whether the storage node is a key network node, and the network measurement data is cached at the key network node. By correlating the basic structure metadata and functional structure metadata in the network data, the degree of correlation between each storage node and other storage nodes is obtained, and the key network nodes in each storage node are determined according to the degree of correlation, so that the The network measurement data is stored in the key network nodes, network monitoring is performed on the key network nodes, and the NDN network is optimized, thereby improving the caching efficiency of the NDN network measurement data.

In this embodiment of the present invention, after acquiring the basic structure metadata and functional structure metadata of each storage node of the NDN network, the basic structure metadata and the functional structure metadata may also be stored in a metadata pool.

Here, the metadata pool is used to cache various types of metadata, and store the basic structure metadata and functional structure metadata in the metadata pool to realize fast data access.

In an optional embodiment of the present invention, based on the functional structure metadata of the two storage nodes and the basic structure metadata in the two storage nodes, the association relationship existing between the two storage nodes is determined, specifically It can be the following four situations:

In the first case, after the infrastructure metadata of one of the two storage nodes responds, the functional structure metadata of the storage node will trigger the response of the infrastructure metadata of the other storage node. There is a sequential relationship between the two storage nodes.

Specifically, the sequence relationship refers to the connection of different infrastructure metadata in a process by certain sequence rules. After one type of infrastructure metadata responds, the next type of infrastructure is triggered through a functional structure metadata. Metadata response. Here, it is determined whether there is an order relationship between the basic structure metadata between the two nodes. When the two storage nodes respond in order, there is an order relationship between the two storage nodes.

In the second case, when the basic structure metadata of the two storage nodes respond at the same time, the functional structure metadata of one of the two storage nodes will trigger the other storage nodes except the two storage nodes. When the infrastructure metadata responds, it is determined that there is a parallel relationship between the two storage nodes.

Specifically, the parallel relationship means that in a process, different infrastructure metadata needs to respond at the same time to trigger the next infrastructure metadata response, and the response process and triggering process are responded by the functional structure metadata. Here, it is determined whether there is a parallel relationship between the two nodes. When the infrastructure metadata of the two storage nodes responds at the same time, if the infrastructure metadata of other nodes can be triggered to respond, the two storage nodes will There is a parallel relationship.

In the third case, when both the basic structure metadata of the two storage nodes respond, the function structure metadata of one storage node in the two storage nodes will trigger a trigger outside the two storage nodes with a preset probability. When the infrastructure metadata of the other storage nodes responds, it is determined that there is a conditional relationship between the two storage nodes.

Specifically, the conditional relationship means that in the case of multiple basic metadata responses in a process, there is a certain probability that multiple basic structure metadata will be triggered to respond respectively, and the response actions during the period are associated with the functional structure metadata. Here, it is determined whether there is a conditional relationship between the two storage nodes. After the infrastructure metadata of the two storage nodes responds, if other storage nodes other than the two storage nodes can be triggered with a preset probability response to the infrastructure metadata, there is a conditional relationship between the two storage nodes.

In the fourth case, after the infrastructure metadata of one storage node of the two storage nodes responds, the infrastructure metadata of the other storage node and the two storage nodes will be triggered through the functional structure metadata of the storage node. When the infrastructure metadata of other storage nodes responds, it is determined that there is a branch relationship between the two storage nodes.

Specifically, the branch relationship means that when one type of infrastructure metadata response is triggered in a process, multiple infrastructure metadata responses will be triggered, and the response actions during the period are associated with the functional structure metadata. Here, it is determined whether there is a branch relationship between the two storage nodes. When one of the infrastructure metadata between the two storage nodes responds, if the infrastructure metadata of the other storage node and the two storage nodes can be triggered. If the infrastructure metadata of other storage nodes other than the storage node responds, there is a branch relationship between the two storage nodes.

In an optional embodiment of the present invention, based on the association relationship between the storage nodes, a topology map of the network relationship between the storage nodes is constructed, which may specifically be:

The storage nodes that have at least one of the sequence relationship, the parallel relationship, the conditional relationship, and the branch relationship between the storage nodes are connected to obtain a network relationship topology diagram between the storage nodes.

Specifically, first determine whether there is an association relationship among the sequence relationship, parallel relationship, conditional relationship, and branch relationship between the storage nodes. and the network relationship topology diagram of the edges connected to each storage node. Here, it is more convenient to calculate the degree of association between each storage node and other storage nodes in each storage node through the network relationship topology diagram.

In an optional embodiment of the present invention, based on the network relationship topology diagram, the degree of association between each storage node in each storage node and other storage nodes is calculated, which may be specifically:

The first step is to calculate the number of storage nodes that have an association relationship between each storage node and other storage nodes in each storage node based on the network relationship topology diagram.

Specifically, in the network relationship topology diagram, if there is an association relationship between a storage node and other nodes, the storage node and other nodes can be connected by lines, and it is calculated that there is an association between the storage node and other storage nodes The number of storage nodes of the relationship can be obtained by calculating the number of connections between the storage node and other storage nodes. The greater the number of connected lines, the greater the number of other storage nodes associated with the storage node, and the more important the storage node is.

In the second step, according to the number of storage nodes, the degree of association between each storage node in each storage node and other storage nodes is determined.

Specifically, after calculating the number of storage nodes that have an association relationship between each storage node and other storage nodes in each storage node, the number of storage nodes is taken as the difference between each storage node and other storage nodes in each storage node. The greater the number of storage nodes, the greater the degree of association between the storage node and other storage nodes, and the more important the storage node is.

In an optional embodiment of the present invention, whether each storage node in each storage node is a key network node is determined based on the degree of association, and specifically, the storage node with the degree of association greater than a preset threshold may be used as the key network node of the NDN network .

Here, the greater the degree of association between the storage node and other storage nodes, the more important the storage node is, that is, when the infrastructure metadata of the storage node responds, more infrastructure metadata of other storage nodes will respond. , or, when the infrastructure metadata of other storage nodes responds, the infrastructure metadata of this storage node will also respond. According to the actual demand, the storage node with the correlation degree greater than the preset threshold is used as the key network node of the NDN network, and then the network measurement data can be stored in the key network node, the network monitoring of the key network node can be performed, and the NDN network can be optimized. The cache efficiency of NDN network measurement data.

Referring to FIG. 2, FIG. 2 is a second schematic flowchart of a NDN network measurement data caching method according to an embodiment of the present invention, including the following steps:

S201, collecting named data network node data.

Specifically, the network data in the named data network collected by the measurement collector, that is, the network node data, mainly includes network status data and transmission content data.

S202, basic structure metadata storage.

Here, the identified metadata is classified, and basic metadata, technical metadata, and structural metadata capable of representing the static characteristics of the storage node are obtained as basic structural metadata.

S203, the function structure metadata is stored.

Here, the identified metadata is classified to obtain, as functional structure metadata, preservation metadata, traceability metadata, management metadata, and process metadata that can represent dynamic characteristics of storage nodes.

S204: Distinguish key network nodes and non-critical network nodes.

Specifically, the functional structure metadata and the basic structure metadata of each two storage nodes in each storage node are used to determine the association relationship between the two storage nodes, and then the association relationship between the storage nodes is used to determine the relationship between the two storage nodes. Build a network relationship topology map between each storage node, and then use the network relationship topology map to calculate the correlation degree between each storage node in each storage node and other storage nodes, and finally determine each storage node according to the correlation degree. Whether each storage node is a key network node, and a storage node with an association degree greater than a preset threshold is regarded as a key network node of the NDN network.

S205, formulate a corresponding caching strategy.

Determine the key network nodes and non-critical network nodes in each storage node according to the correlation degree, and use the storage node with the correlation degree greater than the preset threshold as the key network node of the NDN network, so that the network measurement data can be stored in the key network nodes. Perform network monitoring on key network nodes and optimize the NDN network, thereby improving the caching efficiency of NDN network measurement data.

Referring to FIG. 3, FIG. 3 is a third schematic flowchart of a method for caching NDN network measurement data according to an embodiment of the present invention, including the following steps:

S301, collect network state data and transmission content data centrally through a measurement collector.

Here, in the NDN network, the network data collected by the measurement collector mainly includes network status data and transmission content data.

S302, perform data identification on the collected data.

Specifically, data identification is performed on the collected data to identify basic metadata, technical metadata, structural metadata, preservation metadata, traceability metadata, management metadata and process metadata.

S303, infrastructure metadata.

Specifically, the identified metadata is classified to obtain basic metadata, technical metadata and structural metadata capable of representing the static characteristics of the storage node as basic structural metadata.

S304, Functional structure metadata.

The identified metadata is classified to obtain preservation metadata, traceability metadata, management metadata and process metadata that can represent the dynamic characteristics of the storage node as functional structure metadata.

S305, associate the basic structure metadata with the functional structure metadata.

Specifically, the association relationship existing between the two storage nodes is determined through the functional structure metadata and the basic structure metadata of each two storage nodes in each storage node.

S306: Calculate the association relationship between the basic structure metadata and the functional structure metadata in each piece of network data.

Specifically, the association relationship existing between the storage nodes is determined through the functional structure metadata and the basic structure metadata of each storage node.

S307: Calculate the degree of association between the basic structure metadata and the two types of metadata in the metadata pool through the association relationship.

S308: Calculate the degree of association between the functional structure metadata and the two types of metadata in the metadata pool through the association relationship.

Specifically, S307 and S308 may use the association relationship between the storage nodes to construct a network relationship topology map between the storage nodes, and then use the network relationship topology map to calculate the relationship between each storage node and other storage nodes in the storage nodes. correlation between.

S309: Distinguish key network nodes and non-critical network nodes.

Specifically, according to the association degree, it is determined whether each storage node in each storage node is a key network node, and a storage node with an association degree greater than a preset threshold is used as a key network node of the NDN network.

S310: Set a corresponding caching strategy according to the key network node and the non-critical network node.

Specifically, network measurement data is cached at key network nodes.

Referring to FIG. 4, FIG. 4 is a schematic structural diagram of an NDN network measurement data buffering device according to an embodiment of the present invention, including the following modules:

The obtaining module 401 is used to obtain the basic structure metadata and functional structure metadata of each storage node of the NDN network, wherein the basic structure metadata represents the static feature of the storage node, and the functional structure metadata represents the dynamic feature of the storage node;

The first determination module 402 is configured to, for every two storage nodes in each storage node, determine the two storage nodes based on the functional structure metadata of the two storage nodes and the infrastructure metadata of the two storage nodes The associations that exist between nodes;

The building module 403 is configured to build a network relationship topology diagram between the storage nodes based on the association relationship between the storage nodes;

A calculation module 404, configured to calculate the degree of association between each storage node and other storage nodes in each storage node based on the network relationship topology diagram;

The second determining module 405 is configured to determine, based on the degree of association, whether each storage node in each storage node is a key network node, and cache network measurement data at the key network node.

It can be seen that, in the NDN network measurement data caching device provided by the embodiment of the present invention, the basic structure metadata and functional structure metadata of each storage node of the NDN network are first obtained by the obtaining module, and then the first determination module is used for each storage node. For each two storage nodes in the storage node, based on the functional structure metadata of the two storage nodes and the basic structure metadata of the two storage nodes, determine the association relationship existing between the two storage nodes, and the building module is based on The association relationship between each storage node, the network relationship topology map between the storage nodes is constructed, and based on the network relationship topology map, the calculation module calculates the association degree between each storage node in each storage node and other storage nodes; Finally, the second determining module determines whether each storage node in each storage node is a key network node based on the correlation degree, and caches the network measurement data at the key network node. By correlating the basic structure metadata and functional structure metadata in the network data, the degree of correlation between each storage node and other storage nodes is obtained, and the key network nodes in each storage node are determined according to the degree of correlation, so that the The network measurement data is stored in the key network nodes, network monitoring is performed on the key network nodes, and the NDN network is optimized, thereby improving the caching efficiency of the NDN network measurement data.

Further, the device also includes:

A storage module for storing infrastructure metadata and functional structure metadata into the metadata pool.

Further, the first determining module 402 includes:

The first determination submodule is used to trigger the response of the infrastructure metadata of another storage node through the functional structure metadata of the storage node after the infrastructure metadata of one storage node of the two storage nodes responds , it is determined that there is a sequential relationship between the two storage nodes;

The second determination sub-module is used to trigger the other storage nodes except the two storage nodes through the functional structure metadata of one storage node in the two storage nodes after the two storage nodes respond simultaneously. When the infrastructure metadata of the storage node responds, it is determined that there is a parallel relationship between the two storage nodes;

The third determination sub-module is configured to trigger the two storage nodes with a preset probability through the functional structure metadata of one of the two storage nodes after the basic structure metadata of the two storage nodes responds When the infrastructure metadata of other storage nodes other than the node responds, it is determined that there is a conditional relationship between the two storage nodes;

The fourth determination sub-module is used to trigger the basic structure metadata of the other storage node and the two storage nodes through the functional structure metadata of the storage node after the response of the basic structure metadata of one of the two storage nodes. When the infrastructure metadata of other storage nodes other than one storage node responds, it is determined that there is a branch relationship between the two storage nodes.

Further, the building module 403 is specifically used for:

The storage nodes that have at least one of the sequence relationship, the parallel relationship, the conditional relationship, and the branch relationship between the storage nodes are connected to obtain a network relationship topology diagram between the storage nodes.

Further, the computing module 404 includes:

The calculation sub-module is used to calculate the number of storage nodes that have an association relationship between each storage node and other storage nodes in each storage node based on the network relationship topology map;

The fifth determination sub-module is used for determining the degree of association between each storage node in each storage node and other storage nodes according to the number of storage nodes.

Further, the second determination module 405 is specifically used for:

The storage node with the correlation degree greater than the preset threshold is regarded as the key network node of the NDN network.

An embodiment of the present invention further provides an electronic device, as shown in FIG. 5 , including a processor 501 , a communication interface 502 , a memory 503 and a communication bus 504 , wherein the processor 501 , the communication interface 502 , and the memory 503 pass through the communication bus 504 complete communication with each other,

a memory 503 for storing computer programs;

When the processor 501 is used to execute the program stored in the memory 503, the following steps are implemented:

Obtain the basic structure metadata and functional structure metadata of each storage node of the NDN network, wherein the basic structure metadata represents the static characteristics of the storage node, and the functional structure metadata represents the dynamic characteristics of the storage node;

For every two storage nodes in each storage node, based on the functional structure metadata of the two storage nodes and the basic structure metadata of the two storage nodes, determine the association relationship existing between the two storage nodes;

Based on the association relationship between the storage nodes, construct a topology map of the network relationship between the storage nodes;

Calculate the degree of association between each storage node and other storage nodes in each storage node based on the network relationship topology diagram;

Based on the association degree, it is determined whether each storage node in each storage node is a key network node, and network measurement data is cached at the key network node.

The communication bus mentioned in the above electronic device may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an Extended Industry Standard Architecture (Extended Industry Standard Architecture, EISA) bus or the like. The communication bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of presentation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

The communication interface is used for communication between the above electronic device and other devices.

The memory may include random access memory (Random Access Memory, RAM), and may also include non-volatile memory (Non-Volatile Memory, NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

The above-mentioned processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; may also be a digital signal processor (Digital Signal Processing, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

It can be seen that, through the electronic device provided by the embodiment of the present invention, the basic structure metadata and functional structure metadata of each storage node of the NDN network are first obtained, and then for every two storage nodes in each storage node, based on the The functional structure metadata of the two storage nodes, and the basic structure metadata in the two storage nodes, determine the association relationship between the two storage nodes, and then construct each storage node based on the association relationship between the storage nodes. The network relationship topology map between nodes, and based on the network relationship topology map, calculate the correlation degree between each storage node in each storage node and other storage nodes; finally, based on the correlation degree, determine whether each storage node in each storage node is Network measurement data is cached for and at key network nodes. By correlating the basic structure metadata and functional structure metadata in the network data, the degree of correlation between each storage node and other storage nodes is obtained, and the key network nodes in each storage node are determined according to the degree of correlation, so that the The network measurement data is stored in the key network nodes, network monitoring is performed on the key network nodes, and the NDN network is optimized, thereby improving the caching efficiency of the NDN network measurement data.

In yet another embodiment provided by the present invention, a computer-readable storage medium is also provided, where instructions are stored in the computer-readable storage medium, when the computer-readable storage medium is run on a computer, the computer is made to execute any one of the above-mentioned embodiments. The described NDN network measurement data caching method. Wherein, the described NDN network measurement data caching method includes:

Obtain the basic structure metadata and functional structure metadata of each storage node of the NDN network, wherein the basic structure metadata represents the static characteristics of the storage node, and the functional structure metadata represents the dynamic characteristics of the storage node;

For every two storage nodes in each storage node, based on the functional structure metadata of the two storage nodes and the basic structure metadata of the two storage nodes, determine the association relationship existing between the two storage nodes;

Based on the association relationship between the storage nodes, construct a topology map of the network relationship between the storage nodes;

Calculate the degree of association between each storage node and other storage nodes in each storage node based on the network relationship topology diagram;

Based on the association degree, it is determined whether each storage node in each storage node is a key network node, and network measurement data is cached at the key network node.

It can be seen that, through the computer-readable storage medium provided by the embodiment of the present invention, the basic structure metadata and the functional structure metadata of each storage node of the NDN network are obtained first, and then the basic structure metadata and the functional structure metadata of each storage node in the NDN network are obtained, and then the , based on the functional structure metadata of the two storage nodes and the basic structure metadata in the two storage nodes, determine the association relationship between the two storage nodes, and then based on the association relationship between the storage nodes, Build a network relationship topology map between each storage node, and based on the network relationship topology map, calculate the correlation degree between each storage node in each storage node and other storage nodes; finally, based on the correlation degree, determine each storage node in each storage node. Whether the storage node is a key network node, and the network measurement data is cached at the key network node. By correlating the basic structure metadata and functional structure metadata in the network data, the degree of correlation between each storage node and other storage nodes is obtained, and the key network nodes in each storage node are determined according to the degree of correlation, so that the The network measurement data is stored in the key network nodes, network monitoring is performed on the key network nodes, and the NDN network is optimized, thereby improving the caching efficiency of the NDN network measurement data.

It should be noted that, in this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any relationship between these entities or operations. any such actual relationship or sequence exists. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

Each embodiment in this specification is described in a related manner, and the same and similar parts between the various embodiments may be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the apparatus, electronic equipment, and computer-readable storage medium, since they are basically similar to the method embodiments, the description is relatively simple.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims (9)

1. A method for caching NDN network measurement data is characterized by comprising the following steps:

acquiring basic structure metadata and functional structure metadata of each storage node of an NDN (named data networking) network, wherein the basic structure metadata represent static characteristics of the storage nodes, and the functional structure metadata represent dynamic characteristics of the storage nodes;

for every two storage nodes in the storage nodes, determining an association relation existing between the two storage nodes based on the functional structure metadata of the two storage nodes and the basic structure metadata in the two storage nodes;

constructing a network relationship topological graph among the storage nodes based on the incidence relationship among the storage nodes;

calculating the association degree between each storage node and other storage nodes in each storage node based on the network relationship topological graph;

determining whether each storage node in the storage nodes is a key network node or not based on the association degree, and caching network measurement data in the key network node;

the determining the association relationship existing between the two storage nodes based on the functional structure metadata of the two storage nodes and the infrastructure structure metadata of the two storage nodes includes:

when the basic structure metadata of one storage node in the two storage nodes responds, triggering the basic structure metadata of the other storage node to respond through the functional structure metadata of the storage node, and determining that a sequential relationship exists between the two storage nodes;

when the basic structure metadata of the two storage nodes simultaneously respond, triggering the basic structure metadata of other storage nodes except the two storage nodes to respond through the functional structure metadata of one storage node of the two storage nodes, and determining that the two storage nodes have a parallel relationship;

when the basic structure metadata of the two storage nodes both respond, triggering the basic structure metadata of other storage nodes except the two storage nodes to respond according to the preset probability through the functional structure metadata of one storage node in the two storage nodes, and determining that a condition relation exists between the two storage nodes;

when the basic structure metadata of one storage node in the two storage nodes responds, the basic structure metadata of the other storage node and the basic structure metadata of other storage nodes except the two storage nodes are triggered to respond through the functional structure metadata of the storage node, and the existence of the branch relationship between the two storage nodes is determined.

2. The method of claim 1, wherein after obtaining infrastructure metadata and functional structure metadata for each storage node of the NDN network, the method further comprises:

storing the infrastructure metadata and the functional structure metadata into a metadata pool.

3. The method according to claim 1, wherein the constructing a network relationship topological graph between the storage nodes based on the association relationship between the storage nodes comprises:

and connecting the storage nodes at least having one incidence relation among the sequence relation, the parallel relation, the conditional relation and the branch relation among the storage nodes to obtain a network relation topological graph among the storage nodes.

4. The method according to claim 1, wherein the calculating the association degree between each storage node and other storage nodes based on the network relationship topological graph comprises:

based on the network relationship topological graph, calculating the number of storage nodes with association relationship between each storage node and other storage nodes in each storage node;

and determining the association degree between each storage node and other storage nodes in each storage node according to the number of the storage nodes.

5. The method of claim 1, wherein determining whether each of the storage nodes is a key network node based on the association comprises:

and taking the storage node with the relevance larger than a preset threshold value as a key network node of the NDN.

6. An NDN network measurement data caching apparatus, the apparatus comprising:

the NDN network comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring basic structure metadata and functional structure metadata of each storage node of the NDN network, the basic structure metadata represents static characteristics of the storage nodes, and the functional structure metadata represents dynamic characteristics of the storage nodes;

a first determining module, configured to determine, for each two storage nodes in the storage nodes, an association relationship existing between the two storage nodes based on functional structure metadata of the two storage nodes and infrastructure structure metadata in the two storage nodes;

the building module is used for building a network relationship topological graph among the storage nodes based on the incidence relationship among the storage nodes;

the computing module is used for computing the association degree between each storage node and other storage nodes in each storage node based on the network relation topological graph;

a second determining module, configured to determine, based on the association degree, whether each storage node in the storage nodes is a key network node, and cache network measurement data in the key network node;

the first determining module includes:

the first determining submodule is used for determining that a sequential relationship exists between the two storage nodes when the basic structure metadata of one storage node in the two storage nodes responds and the basic structure metadata of the other storage node responds by triggering the functional structure metadata of the storage node;

the second determining submodule is used for determining that the two storage nodes have a parallel relation when the basic structure metadata of one of the two storage nodes triggers the basic structure metadata of other storage nodes except the two storage nodes to respond through the functional structure metadata of the storage node after the basic structure metadata of the two storage nodes respond simultaneously;

the third determining submodule is used for determining that a condition relation exists between the two storage nodes when the basic structure metadata of the two storage nodes respond and the basic structure metadata of other storage nodes except the two storage nodes are triggered to respond according to the functional structure metadata of one storage node in the two storage nodes by a preset probability;

and the fourth determining submodule is used for determining that a branch relationship exists between the two storage nodes when the basic structure metadata of one storage node in the two storage nodes responds and the basic structure metadata of the other storage node is triggered to respond through the functional structure metadata of the storage node and the basic structure metadata of the other storage nodes except the two storage nodes.

7. The apparatus of claim 6, further comprising:

and the storage module is used for storing the basic structure metadata and the functional structure metadata into a metadata pool.

8. An electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus;

the memory is used for storing a computer program;

the processor, when executing the program stored in the memory, implementing the method steps of any of claims 1-5.

9. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of the claims 1-5.

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