CN107659653B - NDN network measurement data caching method and device, electronic equipment and storage medium - Google Patents
NDN network measurement data caching method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a device for caching NDN network measurement data, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring basic structure metadata and functional structure metadata of each storage node of the NDN; for every two storage nodes in each storage node, 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; and determining whether each storage node in the storage nodes is a key network node or not based on the association degree, and caching the network measurement data in the key network node. By applying the embodiment of the invention, the caching efficiency of the NDN network measurement data can be improved.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a method and an apparatus for caching NDN network measurement data, an electronic device, and a storage medium.
Background
At present, the increasing user scale and user requirements bring huge challenges to the internet, so that the content itself becomes more and more the center of the user requirements, and based on this, NDN (Named Data Network) comes up. The network measurement data are cached in the NDN, so that the NDN can be optimized, and the availability of the NDN is improved. The network measurement data is mainly used for monitoring the network, including monitoring the network operation condition, monitoring the network resources, monitoring the network performance (such as service throughput, time delay, packet loss rate, RTT, bandwidth utilization rate, network scalability, and the like), and can submit fault and abnormal event reports to make corresponding evaluations.
The existing NDN network measurement data caching method is to cache network measurement data on each storage node. Specifically, in the NDN network, each storage node corresponds to a server, and network measurement data is cached in each server, so that network performance data of the corresponding server is measured through the network measurement data, and the NDN network is monitored.
However, in the existing NDN network measurement data caching method, since the network performance data on each server is updated in real time, the network performance data measured in a preset time is more, and whether the corresponding server is available or not is determined by calculating the network performance data, so that not only the calculation amount is large, but also the time consumption is long, and finally the NDN network measurement data caching efficiency is low.
Disclosure of Invention
An object of the embodiments of the present invention is to provide a method and an apparatus for caching NDN network measurement data, an electronic device, and a storage medium, so as to improve the caching efficiency of the NDN network measurement data. The specific technical scheme is as follows:
the embodiment of the invention discloses a method for caching NDN network measurement data, which comprises 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;
and 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.
Optionally, after obtaining the infrastructure metadata and the functional structure metadata of each storage node of the NDN network, the method further includes:
storing the infrastructure metadata and the functional structure metadata into a metadata pool.
Optionally, determining an association relationship existing between the two storage nodes based on the functional structure metadata of the two storage nodes and the infrastructure structure metadata in 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.
Optionally, the constructing a network relationship topology graph between the storage nodes based on the association relationship between the storage nodes includes:
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.
Optionally, the calculating, based on the network relationship topological graph, an association degree between each storage node in the storage nodes and other storage nodes includes:
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.
Optionally, the determining, based on the association degree, whether each storage node in the storage nodes is a key network node includes:
and taking the storage node with the relevance larger than a preset threshold value as a key network node of the NDN.
The embodiment of the invention also discloses a device for caching the NDN network measurement data, which comprises:
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;
and the second determining module is used for 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.
Optionally, the apparatus further comprises:
and the storage module is used for storing the basic structure metadata and the functional structure metadata into a metadata pool.
Optionally, 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.
Optionally, the building module is specifically configured to:
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.
Optionally, the calculation module includes:
the computing submodule is used for computing the number of storage nodes with incidence relation between each storage node and other storage nodes in each storage node based on the network relation topological graph;
and the fifth determining submodule is used for determining the association degree between each storage node and other storage nodes in each storage node according to the number of the storage nodes.
Optionally, the second determining module is specifically configured to:
and taking the storage node with the relevance larger than a preset threshold value as a key network node of the NDN.
The embodiment of the invention also discloses electronic equipment which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory finish mutual communication through the communication bus;
the memory is used for storing a computer program;
the processor is configured to implement the steps of the NDN network measurement data caching method when executing the program stored in the memory.
In yet another aspect of the present invention, a computer-readable storage medium is also disclosed, which has instructions stored therein, which when run on a computer, cause the computer to perform any one of the NDN network measurement data caching methods described above.
According to the NDN network measurement data caching method, device, electronic equipment and storage medium provided by the embodiment of the invention, basic structure metadata and functional structure metadata of each storage node of an NDN network are firstly obtained, then for each two storage nodes in each storage node, based on the functional structure metadata of the two storage nodes and the basic structure metadata in the two storage nodes, an association relation existing between the two storage nodes is determined, then based on the association relation between the storage nodes, a network relation topological graph between the storage nodes is constructed, and based on the network relation topological graph, the association degree between each storage node and other storage nodes in each storage node is calculated; and finally, 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 association degree between each storage node and other storage nodes is obtained by associating the basic structure metadata and the functional structure metadata in the network data, so that the key network nodes in each storage node are determined according to the association degree, the network measurement data are stored in the key network nodes, the network monitoring is carried out on the key network nodes, the NDN is optimized, and the caching efficiency of the NDN network measurement data is improved. Of course, it is not necessary for any product or method of practicing the invention to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a first flowchart illustrating a method for caching measurement data of an NDN network according to an embodiment of the present invention;
fig. 2 is a second flowchart illustrating a method for caching measurement data of an NDN network according to an embodiment of the present invention;
fig. 3 is a third flowchart illustrating a method for caching measurement data of an NDN network according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an NDN network measurement data caching apparatus 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 Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
With the development of mobile wireless networks and the popularization of portable mobile devices, the demand for wireless networks has been satisfied only by end-to-end communication. Today, communication modes that are centered on information content are becoming more and more popular. The named data network originates from a branch of the content-centric network, and unlike the conventional IP network, which reduces communication latency and communication interruption by improving the quality of end-to-end communication links, the communication mode of the named data network focuses more on how to obtain content information faster than where it is obtained. The network measurement data are cached by each storage node in the NDN, so that the network can be monitored in time, and the availability of the NDN is improved. However, the existing NDN network measurement data caching method results in low NDN network measurement data caching efficiency. Therefore, in order to solve the problem of the NDN network measurement data caching efficiency, the invention provides the NDN network measurement data caching method, which can optimize the caching strategy of the NDN network according to the incidence relation of the network storage nodes and further improve the caching efficiency of the network measurement data. The specific scheme is as follows:
referring to fig. 1, fig. 1 is a first flowchart illustrating a method for caching measurement data of an NDN network according to an embodiment of the present invention, including the following steps:
s101, obtaining basic structure metadata and functional structure metadata of each storage node of the NDN, wherein the basic structure metadata represent static characteristics of the storage nodes, and the functional structure metadata represent dynamic characteristics of the storage nodes.
Specifically, in the NDN network, network data mainly comes from local detection data collected by each node deployment client, and the network data includes, but is not limited to, network performance data, node performance data, transmission control data, node transmission content, network log information, and the like. These network data may be divided into basic metadata, technical metadata, structural metadata, preservation metadata, traceability metadata, administrative metadata, and procedural metadata. The basic metadata represent static feature descriptions of data of the storage nodes, the technical metadata represent technical parameter descriptions of the storage nodes, the structural metadata represent the affiliation of the storage nodes, the preservation metadata represent version features of the basic metadata and environment feature descriptions surrounding the versions, the traceability metadata represent context descriptions and environment descriptions of the recorded network data in the using process, the management metadata represent management actions or operation descriptions in the network data, and the procedural metadata represent change information descriptions generated in the processing process of the network data.
The network data are classified in advance, basic metadata, technical metadata and structural metadata which can represent static characteristics of the storage nodes are used as basic structure metadata, and storage metadata, traceability metadata, management metadata and procedural metadata which can represent dynamic characteristics of the storage nodes are used as functional structure metadata, namely the network data are divided into the basic structure metadata and the functional structure metadata.
S102, aiming at every two storage nodes in each storage node, determining the 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.
Specifically, whether each two storage nodes in each storage node have an association relationship is determined by infrastructure metadata and functional structure metadata in the two storage nodes, for example, a response of infrastructure metadata in one of the two storage nodes is related to a response of infrastructure metadata in the other storage node, and may be a simultaneous response or a sequential response, where the responses are 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, and the repeated determination of the association relationship between the storage nodes is avoided.
S103, constructing a network relationship topological graph among the storage nodes based on the incidence relationship among the storage nodes.
Specifically, the network relationship topological graph includes storage nodes and edges connected to the storage nodes, where the edges or lines represent association relationships between the storage nodes, and the storage nodes having the association relationships may be connected together by the lines to form the edges having the association relationships, so that the network relationship topological graph is formed by the storage nodes and the edges. Here, the incidence relation between the storage nodes is reflected by a network relation topological graph, and the incidence relation between each storage node and other storage nodes can be intuitively and conveniently obtained.
S104, calculating the association degree between each storage node and other storage nodes based on the network relationship topological graph.
Specifically, the association degree indicates the association degree of each storage node with other storage nodes in each storage node, that is, the more other storage nodes having an association relationship with one storage node, the greater the association degree of the storage node. Therefore, in the network relationship topological graph, the number of the association relations between each storage node and other storage nodes in each storage node is calculated, the association degree between each storage node and other storage nodes in each storage node can be obtained, and the number of the edges or lines having the association relations between each storage node and other storage nodes in each storage node can also be calculated. Here, by calculating the degree of association between each storage node and another storage node, the degree of importance of each storage node can be quickly determined.
S105, determining whether each storage node in the storage nodes is a key network node or not based on the relevance, and caching network measurement data in the key network node.
Specifically, the association degree indicates the number of other storage nodes having an association relationship with one storage node, and the more the other storage nodes having an association relationship, the greater the association degree of the storage node, and the more the storage node is a key network node. Similarly, the fewer other storage nodes having an association relationship, the smaller the association degree of the storage node, and the storage node is a non-critical network node. After the key network node is determined, the network measurement data can be cached in the key network node, so that the network monitoring can be performed on the key network node through the network measurement data, the NDN is optimized, and the caching efficiency of the NDN network measurement data is improved.
Therefore, according to the NDN network measurement data caching method provided by the embodiment of the invention, the basic structure metadata and the functional structure metadata of each storage node of the NDN network are obtained, then for each two storage nodes in each storage node, 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, then based on the association relationship between the storage nodes, the network relationship topological graph between the storage nodes is constructed, and based on the network relationship topological graph, the association degree between each storage node and other storage nodes in each storage node is calculated; and finally, 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 association degree between each storage node and other storage nodes is obtained by associating the basic structure metadata and the functional structure metadata in the network data, so that the key network nodes in each storage node are determined according to the association degree, the network measurement data are stored in the key network nodes, the network monitoring is carried out on the key network nodes, the NDN is optimized, and the caching efficiency of the NDN network measurement data is improved.
In the embodiment of the present invention, after the infrastructure metadata and the functional structure metadata of each storage node of the NDN network are acquired, the infrastructure metadata and the functional structure metadata may be further stored in a metadata pool.
The metadata pool is used for caching various types of metadata, and storing the basic structure metadata and the functional structure metadata into the metadata pool, so that the data can be rapidly accessed.
In an optional embodiment of the present invention, the association relationship existing between the two storage nodes is determined based on the functional structure metadata of the two storage nodes and the infrastructure structure metadata in the two storage nodes, which may specifically be the following four cases:
in the first case, when the infrastructure metadata of one of the two storage nodes responds, the functional structure metadata of the storage node triggers the infrastructure metadata of the other storage node to respond, and it is determined that the sequential relationship exists between the two storage nodes.
Specifically, the sequential relationship means that different infrastructure metadata are connected in series by a certain sequential rule in one process, and after one infrastructure metadata responds, the next infrastructure metadata responds by one functional structure metadata. Here, it is determined whether the infrastructure metadata between two nodes has a sequential relationship, and when two storage nodes respond in sequence, a sequential relationship exists between the two storage nodes.
In the second case, after the infrastructure metadata of the two storage nodes respond simultaneously, the functional structure metadata of one of the two storage nodes triggers the infrastructure metadata of other storage nodes except the two storage nodes to respond, and it is determined that the two storage nodes have a parallel relationship.
Specifically, the parallel relationship means that different infrastructure metadata in one process need to be responded simultaneously to trigger the next infrastructure metadata response, and the process of responding and the triggering process are responded by the functional structure metadata. Here, whether a parallel relationship exists between two nodes is determined, and when the infrastructure metadata of the two storage nodes simultaneously respond, if the infrastructure metadata of other nodes can be triggered to respond, the parallel relationship exists between the two storage nodes.
In a third case, after the infrastructure metadata of the two storage nodes both respond, triggering the infrastructure metadata of other storage nodes except the two storage nodes to respond according to the functional structure metadata of one storage node in the two storage nodes with a preset probability, and determining that a conditional relationship exists between the two storage nodes.
Specifically, the conditional relationship means that, in the case of multiple types of basic metadata responding in one flow, multiple types of basic structure metadata are triggered to respond respectively with a certain probability, and the responding actions in the period are associated by the functional structure metadata. Here, whether a conditional relationship exists between the two storage nodes is determined, and when the infrastructure metadata of the two storage nodes both respond, if the infrastructure metadata of other storage nodes except the two storage nodes can be triggered to respond with a preset probability, the conditional relationship exists between the two storage nodes.
In a fourth case, when the infrastructure metadata of one of the two storage nodes responds, the infrastructure metadata of the other storage node and the infrastructure metadata of other storage nodes except the two storage nodes respond triggered by the functional structure metadata of the storage node, and it is determined that a branching relationship exists between the two storage nodes.
Specifically, a branch relation refers to a process in which, when one type of infrastructure metadata response is triggered, a plurality of types of infrastructure metadata responses are triggered, and response actions during the process are associated by functional structure metadata. Here, it is determined whether a branching relationship exists between the two storage nodes, and when one of the infrastructure metadata between the two storage nodes responds, if the response of the infrastructure metadata of the other storage node and the infrastructure metadata of the other storage nodes other than the two storage nodes can be triggered, the branching relationship exists between the two storage nodes.
In an optional embodiment of the present invention, the network relationship topological graph between the storage nodes is constructed based on the association relationship between the storage nodes, and specifically, the network relationship topological graph may be:
and connecting the storage nodes with at least one incidence relation among a sequence relation, a parallel relation, a condition relation and a branch relation among the storage nodes to obtain a network relation topological graph among the storage nodes.
Specifically, whether an association relationship among a sequence relationship, a parallel relationship, a conditional relationship and a branch relationship exists among the storage nodes is judged, if so, the storage nodes with the association key are connected, and a network relationship topological graph comprising the storage nodes and edges connected with the storage nodes is obtained. Here, the degree of association between each storage node and other storage nodes in each storage node is more conveniently calculated through the network relationship topological graph.
In an optional embodiment of the present invention, based on the network relationship topological graph, the association degree between each storage node in each storage node and another storage node is calculated, which may specifically be:
the first step is that the number of storage nodes with incidence relation between each storage node and other storage nodes in each storage node is calculated based on a network relation topological graph.
Specifically, in the network relationship topological graph, if an association relationship exists between one storage node and other nodes, the storage node and the other nodes may be connected through a line, and the number of the storage nodes having the association relationship between the storage node and the other storage nodes is calculated, which may be obtained by calculating the number of the connection lines between the storage node and the other storage nodes. The larger the number of the connected lines is, the larger the number of other storage nodes having an association with the storage node is, the more important the storage node is.
And secondly, determining the association degree between each storage node and other storage nodes in each storage node according to the number of the storage nodes.
Specifically, after calculating the number of storage nodes having an association relationship between each storage node and another storage node in each storage node, the number of storage nodes is taken as the magnitude of the association degree between each storage node and another storage node in each storage node, and the greater the number of storage nodes is, the greater the association degree between each storage node and another storage node is, the more important the storage node is.
In an optional embodiment of the present invention, based on the association degree, it is determined whether each storage node in the storage nodes is a key network node, and specifically, the storage node whose association degree is greater than a preset threshold may be used as a key network node of the NDN network.
Here, the greater the degree of association between the storage node and another storage node, the more important the storage node is, that is, when the infrastructure metadata of the storage node responds, the more infrastructure metadata of another storage node responds, or when the infrastructure metadata of another storage node responds, the infrastructure metadata of the storage node also responds. According to actual requirements, the storage nodes with the relevance degree larger than the preset threshold value are used as key network nodes of the NDN, so that the network measurement data can be stored in the key network nodes, network monitoring is carried out on the key network nodes, the NDN is optimized, and therefore the caching efficiency of the NDN network measurement data is improved.
Referring to fig. 2, fig. 2 is a second flowchart illustrating a method for caching measurement data of an NDN network 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, i.e., the network node data, collected by the measurement collector mainly includes network state data and transmission content data.
S202, storing the infrastructure metadata.
Here, the identified metadata is classified to obtain basic metadata, technical metadata, and structural metadata that can represent static characteristics of the storage nodes as infrastructure metadata.
S203, storing the functional structure metadata.
Here, the identified metadata is classified to obtain, as functional structure metadata, storage metadata, traceability metadata, management metadata, and procedural metadata that can represent dynamic characteristics of the storage node.
And S204, distinguishing the key network nodes from the non-key network nodes.
Specifically, an association relationship existing between two storage nodes is determined through functional structure metadata and basic structure metadata of every two storage nodes in each storage node, then a network relationship topological graph between the storage nodes is constructed by utilizing the association relationship between the storage nodes, then the association degree between each storage node and other storage nodes in each storage node is calculated by utilizing the network relationship topological graph, finally whether each storage node in each storage node is a key network node or not is determined according to the association degree, and the storage node with the association degree larger than a preset threshold value is used as the key network node of the NDN.
S205, making a corresponding caching strategy.
And determining key network nodes and non-key network nodes in each storage node according to the association degree, and taking the storage nodes with the association degree larger than a preset threshold value as the key network nodes of the NDN, so that the network measurement data are stored in the key network nodes, network monitoring on the key network nodes is realized, the NDN is optimized, and the caching efficiency of the NDN measurement data is improved.
Referring to fig. 3, fig. 3 is a third schematic flow chart of a NDN network measurement data caching method according to an embodiment of the present invention, including the following steps:
s301, network state data and transmission content data are collected in a centralized mode through the measuring collector.
Here, in the NDN network, the network data collected by the measurement collector mainly includes network status data and transmission content data.
And S302, performing data identification on the acquired data.
Specifically, data identification is carried out on the collected data, and basic metadata, technical metadata, structural metadata, preservation metadata, traceability metadata, management metadata and procedural metadata are identified.
S303, infrastructure metadata.
Specifically, the identified metadata is classified to obtain basic metadata, technical metadata, and structural metadata that can represent static characteristics of the storage nodes as infrastructure metadata.
S304, functional structure metadata.
And classifying the identified metadata to obtain the storage metadata, the traceability metadata, the management metadata and the procedural metadata which can represent the dynamic characteristics of the storage nodes as functional structure metadata.
S305, associating the infrastructure metadata with the functional structure metadata.
Specifically, the association relationship existing between two storage nodes is determined through the functional structure metadata and the infrastructure structure metadata of each two storage nodes.
S306, calculating the incidence relation 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 infrastructure metadata of the storage nodes.
S307, calculating the association degree of the basic structure metadata and the two types of metadata in the metadata pool through the association relation.
And S308, calculating the association degree of the functional structure metadata and the two types of metadata in the metadata pool through the association relation.
Specifically, S307 and S308 may construct a network relationship topological graph between the storage nodes by using the association relationship between the storage nodes, and then calculate the association degree between each storage node in the storage nodes and other storage nodes by using the network relationship topological graph.
S309, distinguishing the key network node from the non-key network node.
Specifically, whether each storage node in each storage node is a key network node is determined according to the association degree, and the storage node with the association degree larger than a preset threshold value is used as the key network node of the NDN.
S310, setting a corresponding caching strategy according to the key network node and the non-key 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 caching apparatus according to an embodiment of the present invention, including the following modules:
an obtaining module 401, configured to obtain infrastructure metadata and functional structure metadata of each storage node of the NDN network, where the infrastructure metadata represents static features of the storage node, and the functional structure metadata represents dynamic features of the storage node;
a first determining module 402, configured to determine, for each two storage nodes in each storage node, an association 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;
a constructing module 403, configured to construct a network relationship topology map between the storage nodes based on the association relationship between the storage nodes;
a calculating module 404, configured to calculate, based on the network relationship topological graph, an association degree between each storage node and another storage node in each storage node;
a second determining module 405, configured to determine whether each storage node in the storage nodes is a key network node based on the association degree, and cache the network measurement data in the key network node.
Therefore, according to the NDN network measurement data caching device provided by the embodiment of the invention, the basic structure metadata and the functional structure metadata of each storage node of the NDN network are firstly obtained through the obtaining module, then the first determining module determines the association relationship existing between the two storage nodes aiming at each two storage nodes, based on the functional structure metadata of the two storage nodes and the basic structure metadata in the two storage nodes, the building module builds the network relationship topological graph between the storage nodes based on the association relationship between the storage nodes, and based on the network relationship topological graph, the computing module computes the association degree between each storage node and other storage nodes in each storage node; and finally, the second determining module determines whether each storage node in the storage nodes is a key network node or not based on the association degree, and caches the network measurement data in the key network node. The association degree between each storage node and other storage nodes is obtained by associating the basic structure metadata and the functional structure metadata in the network data, so that the key network nodes in each storage node are determined according to the association degree, the network measurement data are stored in the key network nodes, the network monitoring is carried out on the key network nodes, the NDN is optimized, and the caching efficiency of the NDN network measurement data is improved.
Further, the apparatus further comprises:
and the storage module is used for storing the basic structure metadata and the functional structure metadata into the metadata pool.
Further, the first determining module 402 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.
Further, the building module 403 is specifically configured to:
and connecting the storage nodes with at least one incidence relation among a sequence relation, a parallel relation, a condition relation and a branch relation among the storage nodes to obtain a network relation topological graph among the storage nodes.
Further, the calculation module 404 includes:
the computing submodule is used for computing the number of storage nodes with incidence relation between each storage node and other storage nodes based on the network relation topological graph;
and the fifth determining submodule is used for determining the association degree between each storage node and other storage nodes in each storage node according to the number of the storage nodes.
Further, the second determining module 405 is specifically configured to:
and taking the storage node with the relevance larger than a preset threshold value as a key network node of the NDN.
An embodiment of the present invention further provides an electronic device, as shown in fig. 5, which includes a processor 501, a communication interface 502, a memory 503 and a communication bus 504, where the processor 501, the communication interface 502 and the memory 503 complete mutual communication through the communication bus 504,
a memory 503 for storing a computer program;
the processor 501, when executing the program stored in the memory 503, implements the following steps:
acquiring basic structure metadata and functional structure metadata of each storage node of the NDN, 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 each storage node, 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;
and determining whether each storage node in the storage nodes is a key network node or not based on the association degree, and caching the network measurement data in the key network node.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
Therefore, according to the electronic device provided by the embodiment of the invention, the basic structure metadata and the functional structure metadata of each storage node of the NDN network are firstly obtained, then, for each two storage nodes in each storage node, the association relationship existing between the two storage nodes is determined based on the functional structure metadata of the two storage nodes and the basic structure metadata in the two storage nodes, then, the network relationship topological graph between the storage nodes is constructed based on the association relationship between the storage nodes, and the association degree between each storage node and other storage nodes in each storage node is calculated based on the network relationship topological graph; and finally, 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 association degree between each storage node and other storage nodes is obtained by associating the basic structure metadata and the functional structure metadata in the network data, so that the key network nodes in each storage node are determined according to the association degree, the network measurement data are stored in the key network nodes, the network monitoring is carried out on the key network nodes, the NDN is optimized, and the caching efficiency of the NDN network measurement data is improved.
In yet another embodiment of the present invention, there is also provided a computer-readable storage medium having stored therein instructions, which when run on a computer, cause the computer to perform a NDN network measurement data caching method as described in any one of the above embodiments. The NDN network measurement data caching method comprises the following steps:
acquiring basic structure metadata and functional structure metadata of each storage node of the NDN, 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 each storage node, 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;
and determining whether each storage node in the storage nodes is a key network node or not based on the association degree, and caching the network measurement data in the key network node.
Therefore, according to 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 first obtained, then, for each two storage nodes in each storage node, 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, then based on the association relationship between the storage nodes, a network relationship topological graph between the storage nodes is constructed, and based on the network relationship topological graph, the association degree between each storage node in each storage node and other storage nodes is calculated; and finally, 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 association degree between each storage node and other storage nodes is obtained by associating the basic structure metadata and the functional structure metadata in the network data, so that the key network nodes in each storage node are determined according to the association degree, the network measurement data are stored in the key network nodes, the network monitoring is carried out on the key network nodes, the NDN is optimized, and the caching efficiency of the NDN network measurement data is improved.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the embodiments of the apparatus, the electronic device, and the computer-readable storage medium, since they are substantially similar to the embodiments of the method, the description is simple, and for the relevant points, reference may be made to the partial description of the embodiments of the method.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within 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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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103179037A (en) * | 2012-12-13 | 2013-06-26 | 清华大学 | Data transmission method of content-centric datacenter network |
| CN103281336A (en) * | 2013-06-19 | 2013-09-04 | 上海众恒信息产业股份有限公司 | Network intrusion detection method |
| CN103906271A (en) * | 2014-04-21 | 2014-07-02 | 西安电子科技大学 | Method for measuring key nodes in Ad Hoc network |
| CN106294156A (en) * | 2016-08-11 | 2017-01-04 | 北京邮电大学 | A kind of static code fault detection analysis method and device |
| CN106982248A (en) * | 2017-03-01 | 2017-07-25 | 中国科学院深圳先进技术研究院 | The caching method and device of a kind of content center network |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7716180B2 (en) * | 2005-12-29 | 2010-05-11 | Amazon Technologies, Inc. | Distributed storage system with web services client interface |
| CN105590001B (en) * | 2015-12-30 | 2019-01-01 | 西北工业大学 | A kind of synthesis importance analysis for the identification of circuit tender spots |
-
2017
- 2017-09-29 CN CN201710905444.3A patent/CN107659653B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103179037A (en) * | 2012-12-13 | 2013-06-26 | 清华大学 | Data transmission method of content-centric datacenter network |
| CN103281336A (en) * | 2013-06-19 | 2013-09-04 | 上海众恒信息产业股份有限公司 | Network intrusion detection method |
| CN103906271A (en) * | 2014-04-21 | 2014-07-02 | 西安电子科技大学 | Method for measuring key nodes in Ad Hoc network |
| CN106294156A (en) * | 2016-08-11 | 2017-01-04 | 北京邮电大学 | A kind of static code fault detection analysis method and device |
| CN106982248A (en) * | 2017-03-01 | 2017-07-25 | 中国科学院深圳先进技术研究院 | The caching method and device of a kind of content center network |
Non-Patent Citations (1)
| Title |
|---|
| 对等网络分布式缓存机制研究;高国强;《中国优秀博士学位论文全文数据库信息科技辑I139-7》;20111015;全文 * |
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