CN114553742B - Network congestion node identification method and system based on ant colony algorithm - Google Patents
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Abstract
The application provides a network congestion node identification method and system based on an ant colony algorithm, comprising the steps of obtaining a network propagation delay matrix and a network queuing delay matrix; constructing a network total delay matrix according to the acquired network propagation delay matrix and the network queuing delay matrix; adopting an ant colony algorithm to circularly search in the total network delay matrix to obtain an optimal path and total network delay in the current network topology; the optimal paths with the common sub-paths are put together to obtain the original optimal paths and the total time delay thereof; randomly reducing the data processing capacity of the network node on the common sub-path in the original optimal path data; obtaining the current optimal path and the total time delay thereof according to the path data with reduced data processing capacity; comparing the original optimal path and the total time delay thereof with the current optimal path and the total time delay thereof to obtain a network congestion node; the application aims to quickly find the congestion node in the network, and provides important help for improving the network communication quality on the basis of the root problem.
Description
Technical Field
The application belongs to the technical field of network communication, and particularly relates to a network congestion node identification method and system based on an ant colony algorithm.
Background
The selection of the network route not only needs to meet different application requirements of users, but also needs to promote the resource utilization of the whole network as much as possible; in order to meet the demands of different users and high-speed transmission of information, network topology structures are becoming larger and more complex; numerous communication network nodes are mutually communicated, and interleaving is complex, so that network congestion is unavoidable; when a problem occurs in one node in the network, the problem may be caused to spread, so that other nodes are congested at the same time; when network congestion occurs, network throughput can be reduced, and even network collapse can occur when the situation is severe. The network congestion phenomenon has great damage to the network, and the overall performance of the network is reduced.
The inventor finds that in the prior art, after a communication network node temporarily fails, most of communication services are satisfied by searching for a suboptimal path; in practice, the delay of the suboptimal path may be large, and the communication quality is greatly reduced compared with the original optimal path; and the huge network communication structure, dense network communication nodes, have great difficulty in directly identifying the congestion nodes.
Disclosure of Invention
In order to solve the problems, the application provides a network congestion node identification method and system based on an ant colony algorithm.
In order to achieve the above object, the present application is realized by the following technical scheme:
in a first aspect, the present application provides a network congestion node identification method based on an ant colony algorithm, including:
acquiring a network propagation delay matrix and a network queuing delay matrix;
constructing a network total delay matrix according to the acquired network propagation delay matrix and the network queuing delay matrix;
adopting an ant colony algorithm to circularly search in the total network delay matrix to obtain an optimal path and total network delay in the current network topology;
combining the optimal paths with the common sub-paths to obtain the original optimal paths and the total time delay thereof;
randomly reducing the data processing capacity of the network node on the common sub-path in the original optimal path data; circularly searching to obtain the current optimal path and the total time delay according to the path data with reduced data processing capacity;
and comparing the original optimal path and the total time delay thereof with the current optimal path and the total time delay thereof to obtain the network congestion node.
Further, the network propagation delay is the ratio of the distance between network nodes to the data transmission speed.
Further, the network queuing delay is obtained through the difference between the service strength and the service rate of the upper node and the data arrival rate, and the service strength is the ratio of the data arrival rate to the service rate of the node.
Further, the data processing capability of a network node refers to the number of data packets that can be processed by the network node per unit time.
Further, the network total delay matrix is obtained by adding a network propagation delay matrix and a network queuing delay matrix.
Further, the ant colony algorithm is adopted to circularly search in the total network delay matrix to obtain the optimal path and the total delay thereof in the current network topology, and the method comprises the following steps:
initializing and assigning initial parameters of an ant colony algorithm;
placing ants into network initial nodes, circularly searching in the network total time delay matrix, and designating network termination nodes;
the ants calculate the transition probability according to the pheromone concentration on the links of the initial node and the adjacent nodes, and check whether the next hop node is in the tabu table of the ants; if not, the node is added into the tabu list of ants, otherwise, the next hop node is reselected;
judging whether the current network node is a network termination node or not; if yes, updating the optimal path, the number of nodes of the optimal path and the total time delay of the optimal path, and then entering subsequent processing; if not, directly entering the subsequent treatment;
updating the pheromone concentration on the link, and automatically increasing the current cycle number by 1;
judging whether the current circulation times are larger than the total circulation times or not; if yes, outputting an optimal path and the total delay of the optimal path; if not, the cyclic search is carried out again.
Furthermore, by comparing the original optimal path with the current optimal path, most of nodes on the current optimal path are the same as the original nodes, and only one or a plurality of nodes are skipped, so that the skipped nodes are inferred to be most likely to be congestion nodes; in addition, the total time delay of the existing optimal path is far longer than that of the original optimal path, and the necessity of identifying the congestion node is proved.
In a second aspect, the present application also provides a network congestion node identification system based on an ant colony algorithm, including:
a data acquisition module configured to: acquiring a network propagation delay matrix and a network queuing delay matrix;
the total delay matrix construction module is used for constructing a network total delay matrix according to the acquired network propagation delay matrix and the network queuing delay matrix;
a loop search module configured to: adopting an ant colony algorithm to circularly search in the total network delay matrix to obtain an optimal path and total network delay in the current network topology;
the original optimal path data acquisition module is configured to: combining the optimal paths with the common sub-paths to obtain the original optimal paths and the total time delay thereof;
the current optimal path data acquisition module is configured to: randomly reducing the data processing capacity of the network node on the common sub-path in the original optimal path data; circularly searching to obtain the current optimal path and the total time delay according to the path data with reduced data processing capacity;
an identification module configured to: and comparing the original optimal path and the total time delay thereof with the current optimal path and the total time delay thereof to obtain the network congestion node.
In a third aspect, the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the network congestion node identification method based on the ant colony algorithm of the first aspect.
In a fourth aspect, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the steps of the network congestion node identification method based on the ant colony algorithm according to the first aspect are implemented when the processor executes the program.
Compared with the prior art, the application has the beneficial effects that:
the application can quickly reduce the searching range of the congestion node and even realize the purpose of directly locking the specific congestion node by the change comparison of the original optimal path and the nodes in the current optimal path and the comparison of the total time delay of the original optimal path and the node; the method has the advantages that important help is provided for timely processing the congestion nodes in the later period, network communication quality is improved on the basis of the root problem, and reliable basis is provided for replacing a mode of meeting communication service by searching a suboptimal path.
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The accompanying drawings, which are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification, illustrate and explain the embodiments and together with the description serve to explain the embodiments.
FIG. 1 is a flow chart of embodiment 1 of the present application;
fig. 2 is a flowchart of the loop search of the ant colony algorithm in the network total delay matrix according to embodiment 1 of the present application.
The specific embodiment is as follows:
the application will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
Example 1:
as shown in fig. 1, this embodiment provides a network congestion node identification method based on an ant colony algorithm, including:
a0: constructing a network topology model, planning distance information among nodes, constructing a network node position distance matrix, acquiring data transmission speed and constructing a network propagation delay matrix;
in this embodiment, the Matlab platform may be based onNA network topology model of the individual nodes, wherein,N>50, the more nodes, the more realistic the data will be; constructing a network node position distance matrixTWherein the unconnected nodes are set as；
In this embodiment, the network propagation delay may be defined as a ratio of a distance between network nodes to a data transmission speed;
a1: acquiring data queuing delay of each network node in the network topology, and constructing a network queuing delay matrix;
in this embodiment, the network queuing delayW q The calculation formula of (2) is as follows:
（1）
（2）
wherein,,W q queuing a time delay for the network,for the intensity of service->Data arrival rate, ++>The service rate of the node;
a2: constructing a network total delay matrix through the network propagation delay matrix and the network queuing delay matrix;
in this embodiment, the total network delay matrix may be obtained by adding the network propagation delay matrix and the network queuing delay matrix; specifically, the network propagation delay can be set to be in the millisecond level, and is much smaller than the network queuing delay, even can be ignored, and the queuing delay at a network node is a critical factor for determining an optimal path;
in the embodiment, the network propagation delay matrix and the network queuing delay matrix are obtained by utilizing the position distance information of the communication network nodes in the network topology system, and the network total delay matrix is constructed, so that the network total delay matrix is conveniently searched in a circulating way through an ant colony algorithm, the optimal path is further obtained efficiently, and the obtained optimal path is more real and accurate;
a3: initializing and assigning initial parameters of an ant colony algorithm;
in this embodiment, the initial parameters include at least one or more of a network start node, a network end node, a distance between nodes, a packet arrival rate, a node service rate, a total number of traversing ants, a total number of cycles, a pheromone factor, a heuristic factor, a pheromone matrix, a pheromone residue, an optimal path node number, and an optimal path total time delay;
when in actual use, the network starting node and the network ending node can be arbitrarily designated, and a large amount of experimental data is obtained by prescribing different starting nodes and ending nodes to compare and verify results; total number of general traversal ants = total number of network nodes 0.5 (rounded off result); the total number of cycles is set according to the network complexity, and the range is generally between tens and hundreds; the pheromone matrix corresponds to the node distance matrixN×NThe matrix of the matrix (1) is a unit matrix, and the residual pheromone is a percentage, generally between 85 and 95 percent;
a4: and circularly searching in the total network delay matrix by adopting the ant colony algorithm to obtain the optimal path and the total delay of the optimal path in the current network topology.
As shown in fig. 2, step A4 includes:
b0: will beOnly ants are put into a network starting node, and the ant colony algorithm is adopted to circularly search for a designated network ending node in the network total time delay matrix;
in actual use, according to the up-down directionTraversing ant numbers as described hereinmTotal number of nodesN*0.5 (rounding the result; number of cycleskAssigning a value of 1; manually designating a network starting node and a network ending node;
b1: the ants calculate the transition probability according to the pheromone concentration on the links of the initial node and the adjacent nodes, and check whether the next hop node is in the tabu table of the ants; if not, the node is added into the tabu list of ants, otherwise, the next hop node is reselected;
b2: judging whether the current network node is a network termination node or not; if yes, executing B3; if not, executing B4;
b3: updating and recording the optimal path, the number of nodes of the optimal path and the total time delay of the optimal path, and then executing the step B4;
when in actual use, starting with a network starting node, searching in a network total time delay matrix by utilizing an ant colony algorithm, and recording a searching path; if the communication network node has traversed, the search is not traversed any more; if traversing to the network termination node, traversing the ants of the current cycle times successfully; and then updating the optimal path, recording the ant traversing path, updating the node number of the optimal path, recording the node number of the ant traversing path, and updating the total delay of the optimal path.
B4: updating the pheromone concentration on the link and the current cycle number is increased by 1;
b5: judging whether the current circulation times are larger than the total circulation times or not; if yes, outputting an optimal path and the total delay of the optimal path; if not, executing B0;
a5: changing different starting nodes and different ending nodes by adopting the ant colony algorithm to obtain a plurality of optimal paths and the total delay of the optimal paths;
a6: placing the optimal paths with the common sub-paths together to form a plurality of groups of data, naming the data as an original optimal path, and storing the original optimal path and the total time delay thereof in an original optimal path set;
a7: selecting a group of data from the original optimal path set, and randomly selecting network nodes on a common sub-path, thereby greatly reducing the data processing capacity of the nodes;
reducing the data processing capacity of the node means that the number of data packets that the network node can process per unit time is reduced.
A8: the ant colony algorithm is operated again, a current optimal path and total time delay of the current optimal path are obtained, and data such as the current optimal path set and the total time delay are put into the current optimal path set;
a9: comparing the original optimal path set with a plurality of groups of data in the current optimal path set one by one; the node which is supposed to be skipped is a congestion node;
in this embodiment, the original optimal path is compared with the current optimal path, one or more nodes are skipped, the skipped nodes are identified as congestion nodes, and meanwhile, the identified congestion nodes are verified by the fact that the total time delay of the optimal path is greater than that of the original optimal path; specifically, when congestion node estimation and identification are performed, the current optimal path and the original optimal path are compared, so that most nodes on the current optimal path are the same as the original nodes, only one or a plurality of nodes are skipped, and the skipped nodes are inferred to be most likely to be congestion nodes; in addition, the total time delay of the existing optimal path is far longer than that of the original optimal path, and the necessity of identifying the congestion node is proved.
In actual use, by comparing multiple groups of experimental data, the identification range of the congestion node can be obviously reduced, and even a specific node can be directly locked under certain conditions.
In this embodiment, as shown in table 1, the paths in the two sets are compared, and it is found that, in each set of data, some nodes in the original optimal path are bypassed by the current optimal path, and these nodes are exactly nodes with drastically reduced data processing capacity after congestion; and the total time delay of the original optimal path can be found to be much smaller by comparing the total time delay of the original optimal path with the total time delay of the existing optimal path.
Table 1: comparing the original optimal path set with partial experimental data in the current optimal path set
Example 2:
the embodiment provides a network congestion node identification system based on an ant colony algorithm, which comprises the following steps:
a data acquisition module configured to: acquiring a network propagation delay matrix and a network queuing delay matrix;
the total delay matrix construction module is used for constructing a network total delay matrix according to the acquired network propagation delay matrix and the network queuing delay matrix;
a loop search module configured to: adopting an ant colony algorithm to circularly search in the total network delay matrix to obtain an optimal path and total network delay in the current network topology;
the original optimal path data acquisition module is configured to: combining the optimal paths with the common sub-paths to obtain the original optimal paths and the total time delay thereof;
the current optimal path data acquisition module is configured to: randomly reducing the data processing capacity of the network node on the common sub-path in the original optimal path data; circularly searching to obtain the current optimal path and the total time delay according to the path data with reduced data processing capacity;
an identification module configured to: and comparing the original optimal path and the total time delay thereof with the current optimal path and the total time delay thereof to obtain the network congestion node.
Example 3:
the present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the ant colony algorithm-based network congestion node identification method described in embodiment 1.
Example 4:
the present embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the steps of the network congestion node identification method based on the ant colony algorithm described in embodiment 1 are implemented when the processor executes the program.
The above description is only a preferred embodiment of the present embodiment, and is not intended to limit the present embodiment, and various modifications and variations can be made to the present embodiment by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment should be included in the protection scope of the present embodiment.
Claims (9)
1. The network congestion node identification method based on the ant colony algorithm is characterized by comprising the following steps of:
acquiring a network propagation delay matrix and a network queuing delay matrix;
constructing a network total delay matrix according to the acquired network propagation delay matrix and the network queuing delay matrix;
adopting an ant colony algorithm to circularly search in the total network delay matrix to obtain an optimal path and total network delay in the current network topology;
combining the optimal paths with the common sub-paths to obtain the original optimal paths and the total time delay thereof;
randomly reducing the data processing capacity of the network node on the common sub-path in the original optimal path data; circularly searching to obtain the current optimal path and the total time delay according to the path data with reduced data processing capacity;
comparing the original optimal path and the total time delay thereof with the current optimal path and the total time delay thereof to obtain a network congestion node;
comparing the original optimal path with the current optimal path, skipping one or more nodes, identifying the skipped nodes as congestion nodes, and simultaneously verifying the identified congestion nodes by the fact that the total time delay of the optimal path is larger than that of the original optimal path.
2. The method for identifying network congestion nodes based on ant colony algorithm as claimed in claim 1, wherein the network propagation delay is a ratio of a distance between network nodes to a data transmission speed.
3. The network congestion node identification method based on the ant colony algorithm according to claim 1, wherein the network queuing delay is obtained by a difference between a service strength and a service rate of an upper node and a data arrival rate; the service strength is the ratio of the data arrival rate to the node service rate.
4. A network congestion node identification method based on the ant colony algorithm as claimed in claim 3, wherein the data processing capacity of the network node refers to the number of data packets that can be processed by the network node per unit time.
5. The method for identifying network congestion nodes based on the ant colony algorithm according to claim 1, wherein the network total delay matrix is obtained by adding a network propagation delay matrix and a network queuing delay matrix.
6. The network congestion node identification method based on the ant colony algorithm as claimed in claim 1, wherein the method for circularly searching in the total network delay matrix by adopting the ant colony algorithm to obtain the optimal path and the total delay thereof in the current network topology comprises the following steps:
initializing and assigning initial parameters of an ant colony algorithm;
placing ants into network initial nodes, circularly searching in the network total time delay matrix, and designating network termination nodes;
the ants calculate the transition probability according to the pheromone concentration on the links of the initial node and the adjacent nodes, and check whether the next hop node is in the tabu table of the ants; if not, the node is added into the tabu list of ants, otherwise, the next hop node is reselected;
judging whether the current network node is a network termination node or not; if yes, updating the optimal path, the number of nodes of the optimal path and the total time delay of the optimal path, and then entering subsequent processing; if not, directly entering the subsequent treatment;
updating the pheromone concentration on the link, and automatically increasing the current cycle number by 1;
judging whether the current circulation times are larger than the total circulation times or not; if yes, outputting an optimal path and the total delay of the optimal path; if not, the cyclic search is carried out again.
7. A network congestion node identification system based on an ant colony algorithm, comprising:
a data acquisition module configured to: acquiring a network propagation delay matrix and a network queuing delay matrix;
the total delay matrix construction module is used for constructing a network total delay matrix according to the acquired network propagation delay matrix and the network queuing delay matrix;
a loop search module configured to: adopting an ant colony algorithm to circularly search in the total network delay matrix to obtain an optimal path and total network delay in the current network topology;
the original optimal path data acquisition module is configured to: combining the optimal paths with the common sub-paths to obtain the original optimal paths and the total time delay thereof;
the current optimal path data acquisition module is configured to: randomly reducing the data processing capacity of the network node on the common sub-path in the original optimal path data; circularly searching to obtain the current optimal path and the total time delay according to the path data with reduced data processing capacity;
an identification module configured to: comparing the original optimal path and the total time delay thereof with the current optimal path and the total time delay thereof to obtain a network congestion node;
comparing the original optimal path with the current optimal path, skipping one or more nodes, identifying the skipped nodes as congestion nodes, and simultaneously verifying the identified congestion nodes by the fact that the total time delay of the optimal path is larger than that of the original optimal path.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the ant colony algorithm-based network congestion node identification method according to any one of claims 1-6.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the ant colony algorithm based network congestion node identification method according to any of claims 1-6 when executing the program.
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