CN110708186B

CN110708186B - Method, device, equipment and medium for evaluating topology fidelity of internet test bed

Info

Publication number: CN110708186B
Application number: CN201910846377.1A
Authority: CN
Inventors: 胡梦琪; 焦波; 张小刚; 江友辉
Original assignee: Guangdong Southern Coast Technology Services Co ltd
Current assignee: Haihua Electronics Enterprise China Corp
Priority date: 2019-09-09
Filing date: 2019-09-09
Publication date: 2022-07-12
Anticipated expiration: 2039-09-09
Also published as: CN110708186A

Abstract

The invention discloses a method, a device, equipment and a medium for evaluating the topology fidelity of an internet test bed, wherein the method comprises the following steps: receiving a test task, and inputting a test index and a corresponding test method into an Internet test bed topology simulation system; inputting the topological characteristics and the corresponding calculation method into the domain of discourse of the Internet test bed topological simulation system; inputting a topology generation tool into a domain of an internet test bed topology simulation system; calculating an importance weight value of each topological feature in a domain relative to the test index, sequencing the importance of all topological features in the domain, and outputting a corresponding importance sequencing vector; extracting the first n topological features in the importance ranking according to the importance ranking vector, and generating a value function; and evaluating the topology fidelity of the topology generation tool by adopting a cost function. The invention provides technical support for feature screening and configuration of topology structure fidelity evaluation, and obtains the result of selecting a topology generation tool in a customized manner for different test tasks.

Description

Method, device, equipment and medium for evaluating topology fidelity of internet test bed

Technical Field

The invention relates to a method, a device, equipment and a medium for evaluating the topology fidelity of an internet test bed, and belongs to the field of characteristic screening and configuration of the internet test bed topology structure fidelity evaluation.

Background

The Internet test bed establishes a simulation test environment for development, test and evaluation of technologies such as resource positioning, access control, routing protocol, active defense and the like. The topological structure represents the interconnection relation among simulation nodes of the test bed, and the fidelity of the topological structure relative to the real world internet topological structure has important influence on the accuracy of a test conclusion on the test bed. At present, the technology for generating the topology structure of the internet test bed mainly comprises a graph sampling algorithm (for example, Random walk, Forest Fire, break-first Search, etc.), and the goal of the technology is to realize the large scale reduction of the real world internet topology scale under the condition of maintaining the stability and invariability of the topology characteristics. The design and evaluation of these techniques relies on a cost function of topological features. However, internet topology has the nontrivial nature of a complex network that possesses an infinite number of topological features (e.g., average, maximum kernel, spectral radius, path length, clustering coefficients, coordination coefficients, algebraic connectivity, weighted spectral distributions, etc.), resulting in the construction of the cost function of existing topology generation techniques often relying on the subjective preferences of researchers (i.e., to subjectively extract a limited subset from the infinite number of topological features), lacking an association with the needs of a particular testing task.

In the existing internet topology generation technology, the internet test bed topology structure large-scale reduction method disclosed in chinese patent application No. CN201810565884.3 takes a complete original internet topology graph as input, and extracts partial nodes and edges from the original topology graph through a graph sampling technology to output a large-scale reduced sampling result graph; the method for reducing the topological structure of the internet test bed in a large scale disclosed in Chinese patent application No. CN201810565884.3 does not sequence the importance of the topological features of the test bed, the evaluation of the scale reduction effect depends on a few of the subjectively selected topological features, no quantitative basis for screening and configuring the topological features is given, and the correlation with the indexes of test tasks is lacked.

Disclosure of Invention

In view of the above, the invention provides a method, a device, equipment and a medium for evaluating the topology fidelity of an internet test bed, which are oriented to the requirements of test tasks, based on equivalent deduction of test indexes, and by quantitatively sequencing the importance of the topology characteristics of the internet test bed, the evaluation on the topology fidelity can be realized, technical support is provided for characteristic screening and configuration of the topology fidelity evaluation, and the result of selecting a topology generation tool in a customized manner for different test tasks is obtained.

The invention aims to provide an internet test bed topology fidelity evaluation method.

The invention also provides a device for evaluating the topological fidelity of the Internet test bed.

It is a third object of the invention to provide a computer apparatus.

It is a fourth object of the invention to provide a storage medium.

The first purpose of the invention can be achieved by adopting the following technical scheme:

an internet test bed topology fidelity evaluation method, the method comprising:

receiving a test task, and inputting a test index and a corresponding test method into an Internet test bed topology simulation system;

inputting the topological characteristics and the corresponding calculation method into the domain of discourse of the Internet test bed topological simulation system; wherein, the discourse domain is a discourse domain oriented to the equivalent deduction of the test index;

inputting a topology generation tool into a domain of an internet test bed topology simulation system;

calculating an importance weight value of each topological feature in a domain relative to the test index, sequencing the importance of all topological features in the domain, and outputting a corresponding importance sequencing vector;

extracting the first n topological features in the importance ranking according to the importance ranking vector, and generating a value function;

and evaluating the topology fidelity of the topology generation tool by adopting a cost function.

Further, the calculating an importance weight value of each topological feature in the domain of discourse relative to the test index, ranking the importance of all the topological features in the domain of discourse, and outputting a corresponding importance ranking vector specifically includes:

for any one quadruple (p)_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Generating a topological graph G (p) by adopting a parallel characteristic pyramid model_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) (ii) a Wherein k is₁＝0,1,L,m_p，k₄＝0,1,L,m_n，q_dAnd delta_dIs a preset parameter;

for the kth topological feature in the theoretical domain, calculating the topological feature in a topological graph G (p)_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Topological characteristic value f of_k(p_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n)；

Using a topological graph G (p)_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Configuring the topological connection relation between simulation nodes on the Internet test bed, and testing to obtain the value x (p) of the test index x_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n)；

Computing the kth topological feature f_kPyramid model parameter p ═ p relative to parallel features_s+k₁·l_pA first spatial recognition capability value of (a);

computing the kth topological feature f_kParameter p for parallel feature pyramid model_s+k₁·l_pAnd a first equivalent deduction value of the test index x;

for any one quadruple (p)_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Generating a topological graph G (p) by adopting a parallel characteristic pyramid model_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) (ii) a Wherein k is₂＝0,1,L,m_q，k₄＝0,1,L,m_n，p_dAnd delta_dIs a preset parameter;

for the kth topological feature in the theoretical domain, calculating the topological feature in a topological graph G (p)_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Topological characteristic value f of_k(p_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n)；

Using a topological graph G (p)_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Configuring the topological connection relation between simulation nodes on the Internet test bed, and testing to obtain the value x (p) of the test index x_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n)；

Computing the kth topological feature f_kPyramid model parameter q ═ q relative to parallel features_s+k₂·l_qA second spatial recognition capability value of (a);

computing the kth topological feature f_kPyramid model parameter q ═ q relative to parallel features_s+k₂·l_qAnd a second equivalent derived measure value for the test index x;

for any one quadruple (p)_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Generating a topological graph G (p) by adopting a parallel characteristic pyramid model_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) (ii) a Wherein k is₃＝0,1,L,m_δ，k₄＝0,1,L,m_n，p_dAnd q is_dIs a preset parameter;

for the kth topological feature in the theoretical domain, calculating the topological feature in a topological graph G (p)_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Topological characteristic value f of_k(p_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n)；

Using a topological graph G (p)_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Configuring the topological connection relation between simulation nodes on the Internet test bed, and testing to obtain the value x (p) of the test index x_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n)；

Computing the kth topological feature f_kPyramid model parameter delta relative to parallel features_s+k₃·l_δA third spatial recognition capability value of (1);

computing the kth topological feature f_kPyramid model parameter delta relative to parallel features_s+k₃·l_δAnd a third equivalent deduction value of the test index x;

calculating the kth topological feature f according to the first space recognition capability value, the second space recognition capability value and the third space recognition capability value_kThe comprehensive space recognition capability value;

calculating the kth topological feature f according to the first equivalent deduction degree value, the second equivalent deduction degree value and the third equivalent deduction degree value_kThe integrated equivalent derived metric value of (2);

calculating the kth topological feature f according to the comprehensive space recognition capability value and the comprehensive equivalent deduction degree value_kImportance weight value relative to test index x.

And sorting the importance of all the topological features in the domain of discourse according to the importance weight value, and outputting a corresponding importance sorting vector.

Further, the k topological feature f is calculated_kPyramid model parameter p ═ p relative to parallel features_s+k₁·l_pThe first spatial recognition capability value of (1), as follows:

wherein,

the k-th of the calculationTopological feature f_kPyramid model parameter p ═ p relative to parallel features_s+k₁·l_pAnd a first equivalent deductive value for the test index x, as follows:

wherein,

the k topological feature f is calculated_kPyramid model parameter q ═ q relative to parallel features_s+k₂·l_qThe second spatial recognition capability value of (1), as follows:

wherein,

the k topological feature f is calculated_kPyramid model parameter q ═ q relative to parallel features_s+k₂·l_qAnd a second equivalent derived measure of test index x, as follows:

wherein,

the k topological feature f is calculated_kPyramid model parameter delta relative to parallel features_s+k₃·l_δThe third spatial recognition capability value of (1), as follows:

wherein,

the k topological feature f is calculated_kPyramid model parameter delta relative to parallel features_s+k₃·l_δAnd a third equivalent derived measure for test index x, as follows:

wherein,

further, the kth topological feature f is calculated according to the first space identification capacity value, the second space identification capacity value and the third space identification capacity value_kThe comprehensive space recognition capability value of (1) is as follows:

wherein,

S_r(f_kp) is a first spatial recognition capability value, S_r(f_kQ) is a second spatial recognition ability value, S_r(f_kδ) is the third spatial recognition capability value.

Further, the kth topological feature f is calculated according to the first equivalent deduction degree value, the second equivalent deduction degree value and the third equivalent deduction degree value_kThe integrated equivalent derived measure value of (a) is as follows:

wherein,

E_x(f_kp) is a first equivalent derived measure, E_x(f_kQ) is a second equivalent derived measure, E_x(f_kδ) is the third equivalent derivative metric value.

Further, the kth topological feature f is calculated according to the comprehensive space identification capacity value and the comprehensive equivalent deduction degree value_kThe importance weight value relative to test index x is given by:

R_x(f_k)＝w_s·S_r(f_k)+w_e·E_x(f_k)

wherein, w_s＝0.5，w_e＝0.5，S_r(f_k) Identifying the capability value for the synthesis space, E_x(f_k) And the equivalent derived measure value is integrated.

Further, the topological feature includes an average node degree f₁Maximum nucleus f₂Average path length f₃Average clustering coefficient f₄Mixed coordination coefficient f₅Algebraic connectivity f₆Natural connectivity f₇Weighted spectral distribution f₈The repetition degree f of the characteristic value 1₉Laplacian spectrum radius f₁₀Adjacent spectral radius f₁₁And the radius f of the regular Laplacian spectrum₁₂。

The second purpose of the invention can be achieved by adopting the following technical scheme:

an apparatus for evaluating the topology fidelity of an internet test bed, the apparatus comprising:

the first input module is used for receiving a test task and inputting a test index and a corresponding test method into the Internet test bed topology simulation system;

the second input module is used for inputting the topological characteristics and the corresponding calculation method into the domain of discourse of the topology simulation system of the Internet test bed; wherein, the discourse domain is a discourse domain oriented to the equivalent deduction of the test index;

the third input module is used for inputting the topology generation tool into the domain of discourse of the topology simulation system of the Internet test bed;

the calculation module is used for calculating the importance weight value of each topological feature in the domain of discourse relative to the test index, sorting the importance of all the topological features in the domain of discourse, and outputting a corresponding importance sorting vector;

the extracting module is used for extracting the first n topological features in the importance sequence according to the importance sequence vector and generating a value function;

and the evaluation module is used for evaluating the topology fidelity of the topology generation tool by adopting a cost function.

The third purpose of the invention can be achieved by adopting the following technical scheme:

a computer device comprises a processor and a memory for storing a program executable by the processor, wherein the processor executes the program stored in the memory to realize the method for evaluating the topological fidelity of the Internet test bed.

The fourth purpose of the invention can be achieved by adopting the following technical scheme:

a storage medium stores a program, and when the program is executed by a processor, the method for evaluating the topology fidelity of the Internet test bed is realized.

Compared with the prior art, the invention has the following beneficial effects:

the invention provides a quantitative internet test bed topological feature importance ordering method by aiming at infinite characteristics of the topological structure of the internet test bed, solving the defects of strong subjectivity of traditional topological feature extraction and configuration and lack of quantitative data support, taking equivalent deduction of specific test indexes as a standard, realizing self-adaptive matching of topological fidelity optimal tools from different topological generation tools according to different test indexes, and providing technical support for accurate testing of internet technologies which strongly depend on the topological structure, such as resource positioning, access control, routing protocols, active defense and the like.

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 structures shown in the drawings without creative efforts.

Fig. 1 is a flowchart of an internet test bed topology fidelity evaluation method according to embodiment 1 of the present invention.

Fig. 2 is a flowchart of calculating an importance weight value of each topological feature relative to a test indicator in a domain of discourse according to embodiment 1 of the present invention.

FIG. 3 is a graph of the importance ranking of topological features relative to the test index "delay rate" in the domain of discourse in embodiment 1 of the present invention.

FIG. 4 is a graph of the ranking of importance of topological features in the domain of discourse relative to the test indicator "cost rate" in accordance with embodiment 1 of the present invention.

Fig. 5 is a block diagram of a topology fidelity evaluation apparatus of an internet test bed in embodiment 2 of the present invention.

Fig. 6 is a block diagram of a computing module according to embodiment 2 of the present invention.

Fig. 7 is a block diagram of a computer device according to embodiment 3 of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.

Example 1:

as shown in fig. 1, the present embodiment provides an internet test bed topology fidelity evaluation method, which includes the following steps:

s101, receiving a test task, and inputting a test index and a corresponding test method into an Internet test bed topology simulation system.

In this step, the test index is denoted as x, which is a specific index for measuring the tested object (such as resource location, admission control, routing protocol, active defense, etc.).

S102, inputting the topological characteristics and the corresponding calculation method into a domain of discourse of the Internet test bed topological simulation system.

In this step, the domain of discourse of the topology simulation system of the internet test bed is marked as Ω, which refers to a topology characteristic domain enumerated in advance by a tester, and the internet topology structure has infinite topological characteristics, so the topological characteristic importance ranking must be oriented to a specific domain of discourse, the domain of discourse Ω of this embodiment is a domain oriented to equivalent deduction of test indexes, wherein the equivalent deduction means that the test conclusion of the test indexes on the scale-down internet test bed is consistent with the operation effect of the test indexes on the real world large-scale internet; it can be seen that the domain Ω is essentially a topological feature set Ω, which contains t topological features enumerated in advance.

By default, the domain of discourse Ω contains twelve common topological features: average nodal degree f₁Maximum nucleus f₂Average path length f₃Average clustering coefficient f₄Mixed coordination coefficient f₅Algebraic connectivity f₆Natural connectivity f₇Weighted spectral distribution f₈Characteristic value 1 repetition degree f₉Laplacian spectrum radius f₁₀Adjacent spectral radius f₁₁And the radius f of the regular Laplacian spectrum₁₂。

Specifically, the calculation method of the twelve topological features of the embodiment is as follows:

and (V, E) representing the internet topology by using a simple undirected graph G, wherein V and E are a node set and an edge set respectively.

Let d_vFor the degree of the node v in the graph G, | S | | | represents the total number of contained elements in the set S.

1) Average sectionPoint degree f₁The calculation method comprises the following steps:

wherein d is_maxFor the maximum node degree in graph G, p (k) represents the percentage of nodes in node set V that are k.

2) Maximum nucleus f₂The calculation method comprises the following steps:

step 1, arranging all nodes in the graph G into v according to the sequence of degree from small to large₁,v₂,L,v_nWherein n ═ V | |;

step 2, initialize i ← 1 (symbol ← indicating assignment of 1 to variable i), G₀And (3) going to step 3.

Step 3, connecting the node v_iFrom subfigure G_i-1Deleting, and setting a subgraph formed by the residual nodes as G_i(ii) a Computation subgraph G_iMinimum node degree k in_i(ii) a And (3) updating i ← i +1, if i is less than n, turning to step 3, otherwise, turning to step 4.

Step 4, calculating k₁,k₂,L,k_n-1Maximum value k in_maxThen f is₂＝k_maxThe algorithm terminates.

3) Average path length f₃The calculation method comprises the following steps:

wherein l_maxIs the maximum value of the shortest path length between any pair of nodes in graph G, P_l(k) The node pair number with shortest path length k in graph G is represented as a percentage of the total number of all node pairs.

4) Average clustering coefficient f₄The calculation method comprises the following steps:

C(k)＝2T_k/k(k-1)，

wherein d is_maxIs the maximum node degree in graph G, P (k) represents the percentage of nodes in node set V with k, C (k) represents the nodes V 'with k in graph G'₁,v′₂,L,v′_tT (v)'_i) (i ═ 1,2, L, t) denotes a node v'_iThe total number of edges between any two adjacent nodes. Note: if T is 0, then T (v'_i)＝0。

5) Coefficient of coordination of mixing f₅The calculation method comprises the following steps:

wherein j is_i,k_iRepresenting the degrees of the two endpoints (nodes) of the ith edge in the edge set E, | | E | | | represents the total number of edges included in the graph G.

6) Algebraic connectivity f₆The calculation method comprises the following steps:

f₆＝λ₂，

wherein λ is₁≤λ₂≤L≤λ_nAll eigenvalues of the Laplacian matrix l (G) D-a of the graph G, D representing all nodes v in the graph G₁,v₂,L,v_nDegree of (1)

L,

Is a diagonal matrix of diagonal elements, A ═ a_ij)_n×nThe adjacency matrix of diagram G is represented, namely: if node v_i,v_jIn graph G, taken together, then a_ij1, otherwise a_ij＝0。

7) Natural connectivity f₇The calculation method comprises the following steps:

wherein, γ₁,γ₂,L,γ_nIs adjacency matrix A ═ of (a) of graph G_ij)_n×nAll characteristic values of (a).

8) Weighted spectral distribution f₈The calculation method comprises the following steps:

wherein eta is₁,η₂,L,η_nNormal Laplacian matrix NL (G) D of fig. G^-1/2(D-A)D^-1/2D represents the total number of nodes v in the graph G₁,v₂,L,v_nDegree of (1)

L,

Is a diagonal matrix of diagonal elements, A ═ a_ij)_n×nThe adjacency matrix of diagram G is represented, namely: if node v_i,v_jIn the graph G are connected, then a_ij1, otherwise a_ij＝0。

9) Repetition degree f of eigenvalue 1₉The calculation method comprises the following steps:

f₉＝g(1)/n，

wherein G (1) represents all eigenvalues η of the regular Laplacian matrix NL (G) of the graph G₁,η₂,L,η_nThe total number of median values is 1.

10) Laplacian spectrum radius f₁₀The calculation method comprises the following steps:

f₁₀＝max(|λ₁|,|λ₂|,L,|λ_n|)，

wherein λ is₁,λ₂,L,λ_nAll eigenvalues of the Laplacian matrix l (G) of the graph G.

Remarking: | g | represents taking the absolute value, and max represents taking the maximum value.

11) Adjacent spectral radius f₁₁The calculating method of (2):

f₁₁＝max(|γ₁|,|γ₂|,L,|γ_n|)，

12) Regular Laplacian spectrum radius f₁₂The calculation method comprises the following steps:

f₁₂＝max(|η₁|,|η₂|,L,|η_n|)，

wherein eta is₁,η₂,L,η_nIs the overall eigenvalue of the regular Laplacian matrix nl (G) of the graph G.

And S103, inputting the topology generation tool into the domain of the topology simulation system of the Internet test bed.

In this step, the topology generation tools include Random walk, Forest Fire, break-first Search, SnowBall, node/edge Random deletion, and the like.

And S104, calculating the importance weight value of each topological feature in the domain relative to the test index, sorting the importance of all topological features in the domain, and outputting a corresponding importance sorting vector.

In order to output the importance ranking vector of all the topological features in the domain of discourse, the present embodiment needs to input a discrete sequence p of a topological Feature set Ω and a Parallel Feature Pyramid model (PFP for short) parameter p_s+k₁·l_p(k₁＝0,1,L,m_p) Discrete sequence q of parallel feature pyramid model parameters q_s+k₂·l_q(k₂＝0,1,L,m_q) Discrete sequence of parallel feature pyramid model parameters delta_s+k₃·l_δ(k₃＝0,1,L,m_δ) A discrete sequence n of the number n of topological nodes_s+k₄·l_n(k₄＝0,1,L,m_n) And a test index x.

The parallel feature pyramid model is a simulation model for generating an internet topological graph by taking quadruples (p, q, delta, n) as input.

By default, (p)_s,l_p,m_p)＝(0.2,0.1,5)，(q_s,l_q,m_q)＝(0.1,0.1,5)，

(δ_s,l_δ,m_δ)＝(0.008,0.02,5)，(n_s,l_n,m_n)＝(1000,500,11)。

The embodiment only focuses on the topological structure of the internet test bed; therefore, the test conclusion of the test index x only depends on the topological connection relation between the simulation nodes of the Internet test bed.

The step of generating the topological graph G in this embodiment is as follows:

inputting: quadruplets (p, q, δ, n);

and (3) outputting: and G, simulating a topological graph.

And step 1, generating a complete graph G containing 9 nodes.

And 2, if the graph G contains the number of nodes smaller than n, turning to the step 3, otherwise, G is the output simulation topological graph.

And 3, generating a random number r between 0 and 1, if r is more than or equal to 0 and less than p, turning to the step 4, if r is more than or equal to p and less than p + q, turning to the step 5, and if r is more than or equal to p + q and less than or equal to 1, turning to the step 6.

Step 4, distributing by probability

Wherein v is_iRepresents the ith node, k, in graph G_iRepresenting a node v_iIn degree of graph G, n (G) represents the total number of nodes contained in graph G), two different nodes w are randomly extracted in graph G₁And w₂And adding a new node v and two new edges (v, w) in the graph G₁)、(v,w₂) (ii) a And (6) turning to the step 2.

Step 5, pi (v) with probability distribution_i) Randomly drawing three different nodes w in graph G₁、w₂And w₃And a new node v is added in the graph GAnd three new edges (v, w)₁)、(w₁,w₂)、(w₁,w₃) (ii) a And (6) turning to the step 2.

Step 6, distributing II (v) according to probability_i) Randomly drawing three different nodes w in graph G₁、w₂And w₃And adding a new node v and three new edges (v, w) in the graph G₁)、(v,w₂)、(w₁,w₃) (ii) a And (6) turning to the step 2.

As shown in fig. 2, the step S104 specifically includes:

s10401, setting parameter p_d＝0.3，q_d＝0.1，δ_d＝0.048。

S10402, for any one quadruple (p)_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Generating a topological graph G (p) by adopting a parallel characteristic pyramid model_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Recording the quadruple as a first quadruple, and taking the topological graph as a first topological graph; wherein k is₁＝0,1,L,m_p，k₄＝0,1,L,m_n。

S10403, for the kth topological feature in the theory domain, calculating the first topological graph G (p) of the topological feature_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Topological characteristic value f of_k(p_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n)。

S10404, adopting a first topological graph G (p)_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Configuring the topological connection relation between simulation nodes on the Internet test bed, and testing to obtain the value x (p) of the test index x_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n)。

S10405, calculating the kth topological feature f_kRelative to the first levelLine characteristic pyramid model parameter p ═ p_s+k₁·l_pIs determined by the first spatial recognition capability value.

Specifically, the kth topological feature f is calculated_kPyramid model parameter p ═ p relative to the first parallel features_s+k₁·l_pA first spatial recognition capability value of (a), as follows:

wherein,

s10406, calculating the kth topological feature f_kPyramid model parameter p ═ p relative to the first parallel features_s+k₁·l_pAnd a first equivalent deduction value for the test index x.

Specifically, the kth topological feature f is calculated_kPyramid model parameter q ═ q relative to the first parallel features_s+k₂·l_qAnd a first equivalent deductive value for the test index x, as follows:

wherein,

s10407, for any one quadruple (p)_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Generating a topological graph G (p) by adopting a parallel characteristic pyramid model_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Recording the quadruple as a second quadruple, and taking the topological graph as a second topological graph; wherein k is₂＝0,1,L,m_q，k₄＝0,1,L,m_n。

S10408, for the kth topological feature in the theoretical domain, calculating the second topological graph G (p) of the topological feature_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Topological characteristic value f of_k(p_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n)。

S10409, adopting a second topological graph G (p)_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Configuring the topological connection relation between simulation nodes on the Internet test bed, and testing to obtain the value x (p) of the test index x_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n)。

S10410, calculating the kth topological feature f_kWith respect to the second parallel feature pyramid model parameter q ═ q_s+k₂·l_qThe second spatial recognition capability value of (1).

Specifically, the kth topological feature f is calculated_kPyramid model parameter q ═ q relative to the second parallel features_s+k₂·l_qA second spatial recognition capability value of (a), as follows:

wherein,

s10411, calculating the kth topological feature f_kPyramid model parameter q ═ q relative to the second parallel features_s+k₂·l_qAnd a second equivalent deduction value for the test index x.

Specifically, the kth topological feature f is calculated_kPyramid model parameter q ═ q relative to the second parallel features_s+k₂·l_qAnd the first of the test index xTwo equivalent derived metric values, as follows:

wherein,

s10412, for any one quadruple (p)_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Generating a topological graph G (p) by adopting a parallel characteristic pyramid model_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Recording the quadruple as a third quadruple, and taking the topological graph as a third topological graph; wherein k is₃＝0,1,L,m_δ，k₄＝0,1,L,m_n。

S10413, for the kth topological feature in the theory domain, calculating the topological feature in the third topological graph G (p)_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Topological characteristic value f of_k(p_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n)。

S10414, adopting a third topological graph G (p)_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Configuring the topological connection relation between simulation nodes on the Internet test bed, and testing to obtain the value x (p) of the test index x_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n)。

S10415, calculating the kth topological feature f_kThe pyramid model parameter delta is delta relative to the third parallel feature_s+k₃·l_δThe third spatial recognition capability value of (1).

Specifically, the kth topological feature f is calculated_kRelative toThe third parallel characteristic pyramid model parameter delta is delta_s+k₃·l_δThe third spatial recognition capability value of (1), as follows:

wherein,

s10416, calculating the kth topological feature f_kThe pyramid model parameter delta is delta relative to the third parallel feature_s+k₃·l_δAnd a third equivalent deduction value for the test index x.

Specifically, the kth topological feature f is calculated_kThe pyramid model parameter delta is delta relative to the third parallel feature_s+k₃·l_δAnd a third equivalent derived measure for test index x, as follows:

wherein,

s10417, calculating the kth topological feature f according to the first space recognition capability value, the second space recognition capability value and the third space recognition capability value_kThe comprehensive space recognition capability value of (1).

Specifically, the kth topological feature f is calculated according to the first space recognition capability value, the second space recognition capability value and the third space recognition capability value_kThe comprehensive space recognition capability value of (1) is as follows:

wherein,

S10418, calculating a kth topological feature f according to the first equivalent deduction degree value, the second equivalent deduction degree value and the third equivalent deduction degree value_kThe integrated equivalent derived measure value.

Specifically, the kth topological feature f is calculated according to the first equivalent deduction degree value, the second equivalent deduction degree value and the third equivalent deduction degree value_kThe integrated equivalent derived measure value of (a) is as follows:

wherein,

E_x(f_kp) is a first equivalent derived measure, E_x(f_kQ) is a second equivalent derived measure value, E_x(f_kδ) is the third equivalent derivative metric value.

S10419, calculating the kth topological feature f according to the comprehensive space identification capacity value and the comprehensive equivalent deduction degree value_kImportance weight value relative to test index x.

Specifically, the kth topological feature f is calculated according to the comprehensive space identification capacity value and the comprehensive equivalent deduction degree value_kThe importance weight value relative to test index x is given by:

R_x(f_k)＝w_s·S_r(f_k)+w_e·E_x(f_k)

wherein w_s＝0.5，w_e＝0.5，S_r(f_k) To synthesize the spatial recognition capability value, E_x(f_k) For comprehensive equivalent deduction degreeThe value is obtained.

And S10420, sorting the importance of all the topological features in the domain of discourse according to the importance weight value, and outputting a corresponding importance sorting vector.

And S105, extracting the first n topological features in the importance sequence according to the importance sequence vector, and generating a cost function.

And S106, evaluating the topology fidelity of the topology generation tool by adopting a cost function.

In particular, assume that G is a real-world topological graph, z₁,z₂,L,z_kCorresponding k topology generation tools, and the simulation graphs generated by the k tools are respectively set as G₁,G₂,L,G_kThen, evaluate z_iThe cost function of (i ═ 1,2, L, k) is an objective function, as follows:

wherein f is₁,f₂,L,f_tIs t topological features, w, arranged by importance₁,w₂,L,w_tAre respectively f₁,f₂,L,f_tOf importance ranking the weight values, f_j(G) And f_j(G_i) Representing the jth topological feature f_jRespectively in the real graph G and the simulation graph G_jThe value of (d).

Computing

Then it can be deduced that: the s th tool z_sIs an optimal topology generation tool.

In step S105 and step S106, the vectors V are sorted according to importance_rankA small number of topological features with high importance (the first three topological features in the importance ranking in the embodiment) can be screened out to form a cost function; the merit function can be used as an evaluation standard for generating topology fidelity by a topology generation tool, and a simulation topology which is closer to a real world internet topological structure in the topological characteristic of the merit function is extracted as a recordEntering a test task of the Internet test bed topology simulation system and an Internet test bed topology environment of a test index; importance ranking vector V_rankThe self-adaptive matching method strongly depends on the test index x, so that the self-adaptive matching of the optimal topological fidelity tool can be realized from different topological generation tools such as Random walk, Forest Fire, break-first Search, SnowBall and Random node/edge deletion according to different test indexes.

In a specific example of this embodiment, the test task is a specific cbt (center Based tree) -oriented multicast routing protocol test task, and Based on equivalent deduction of two specific test indexes, namely delay rate and cost rate, by using the quantitative ranking method for importance of the topology features of the internet test bed of this embodiment, the importance of twelve topology features in the domain Ω is ranked under a default condition, and the ranking result is shown in fig. 3 and fig. 4, which shows that the ranking of the importance of the topology features is usually different for different test indexes, that is, the selection of the cost function composed of the topology features used in the fidelity evaluation of the topology structure of the internet test bed strongly depends on the specific test task and test indexes. The technology of the embodiment is applied to the self-adaptive matching selection of the test bed topology generation tool under different test task requirements, and has important application value for the accurate test of the internet technology which strongly depends on the topology structure, such as resource positioning, access control, routing protocol, active defense and the like.

Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be implemented by a program to instruct associated hardware, and the corresponding program may be stored in a computer-readable storage medium.

It should be noted that although the method operations of the above-described embodiments are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Rather, the depicted steps may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.

Example 2:

as shown in fig. 5, the present embodiment provides an apparatus for evaluating internet test bed topology fidelity, the apparatus includes a first entry module 501, a second entry module 502, a third entry module 503, a calculation module 504, an extraction module 505, and an evaluation module 506, and specific functions of each module are as follows:

the first entry module 501 is configured to receive a test task and enter a test index and a corresponding test method into the internet test bed topology simulation system.

The second entry module 502 is configured to enter the topological feature and the corresponding calculation method into a domain of the topology simulation system of the internet test bed; wherein, the discourse domain is a discourse domain oriented to the equivalent deduction of the test indexes.

The third entering module 503 is configured to enter a topology generating tool into a domain of discourse of the internet test bed topology simulation system.

The calculating module 504 is configured to calculate an importance weight value of each topological feature in the domain relative to the test indicator, rank the importance of all topological features in the domain, and output a corresponding importance ranking vector.

The extracting module 505 is configured to extract the top n topological features in the importance ranking according to the importance ranking vector, and generate a cost function.

The evaluation module 506 is configured to evaluate the topology fidelity of the topology generation tool by using a cost function.

Further, the calculating module 504 specifically includes:

a first generating unit 50401 for generating a four-tuple (p) for any one_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Generating a topological graph G (p) by adopting a parallel characteristic pyramid model_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) (ii) a Wherein k is₁＝0,1,L,m_p，k₄＝0,1,L,m_n，q_dAnd delta_dIs a preset parameter.

A first calculating unit 50402, configured to calculate, for the kth topological feature in the theoretical domain, the topological feature in the topological graph G (p)_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Topological characteristic value f of_k(p_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n)。

A first test unit 50403 for employing the topology map G (p)_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Configuring the topological connection relation between simulation nodes on the Internet test bed, and testing to obtain the value x (p) of the test index x_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n)；

A second calculating unit 50404 for calculating the kth topological feature f_kPyramid model parameter p ═ p relative to parallel features_s+k₁·l_pIs determined by the first spatial recognition capability value.

A third calculating unit 50405 for calculating the kth topological feature f_kPyramid model parameter p ═ p relative to parallel features_s+k₁·l_pAnd a first equivalence deduction value for the test index x.

A second generating unit 50406, for generating a four-tuple (p) for any one_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Generating a topological graph G (p) by adopting a parallel characteristic pyramid model_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) (ii) a Wherein k is₂＝0,1,L,m_q，k₄＝0,1,L,m_n，p_dAnd delta_dAre preset parameters.

A fourth calculation unit 50407, configured to calculate, for a kth topological feature in the theory domain, the topological feature in the topological graph G (p)_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Topological characteristic value f of_k(p_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n)。

A second test unit 50408 for employing the topology G (p)_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Configuring the topological connection relation between simulation nodes on the Internet test bed, and testing to obtain the value x (p) of the test index x_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n)。

A fifth calculating unit 50409 for calculating the kth topological feature f_kRelative to the parameter q of the parallel characteristic pyramid model, q is q_s+k₂·l_qThe second spatial recognition capability value of (2).

A sixth calculating unit 50410, configured to calculate a kth topological feature f_kPyramid model parameter q ═ q relative to parallel features_s+k₂·l_qAnd a second equivalent derived metric value for test metric x.

A third generating unit 50411 for generating a four-tuple (p) for any one of the four tuples_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Generating a topological graph by adopting a parallel characteristic pyramid model; wherein k is₃＝0,1,L,m_δ，k₄＝0,1,L,m_n，p_dAnd q is_dIs a preset parameter.

A seventh calculating unit 50412, configured to calculate, for the kth topological feature in the theoretical domain, the topological feature in the topological graph G (p)_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Topological characteristic value f of_k(p_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n)。

A third test unit 50413 for employing a topologyGraph G (p)_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Configuring the topological connection relation between simulation nodes on the Internet test bed, and testing to obtain the value x (p) of the test index x_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n)。

An eighth calculating unit 50414 for calculating a kth topological feature f_kPyramid model parameter delta relative to parallel features_s+k₃·l_δThe third spatial recognition capability value of (1).

A ninth calculating unit 50415 for calculating a kth topological feature f_kParameter delta is equal to delta relative to parallel characteristic pyramid model_s+k₃·l_δAnd a third equivalent derivative value for the test index x.

A tenth calculating unit 50416, configured to calculate the kth topological feature f according to the first space recognition capability value, the second space recognition capability value and the third space recognition capability value_kThe comprehensive space recognition capability value of (2).

An eleventh calculating unit 50417, configured to calculate the kth topological feature f according to the first equivalent derived metric value, the second equivalent derived metric value, and the third equivalent derived metric value_kThe integrated equivalent derived measure value.

A twelfth calculating unit 50418, configured to calculate the kth topological feature f according to the integrated space recognition capability value and the integrated equivalent deduction degree value_kImportance weight value relative to test index x.

And the output unit 50419 is used for sorting the importance of all the topological features in the domain of interest according to the importance weight value and outputting a corresponding importance sorting vector.

The specific implementation of each module in this embodiment may refer to embodiment 1, which is not described herein any more; it should be noted that, the apparatus provided in this embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure is divided into different functional modules to complete all or part of the functions described above.

Example 3:

the present embodiment provides a computer device, which is the local computer described in embodiment 1 above, and as shown in fig. 7, the computer device includes a processor 702, a memory, an input device 703, a display 704, and a network interface 705, which are connected by a system bus 701, the processor is configured to provide computing and control capabilities, the memory includes a nonvolatile storage medium 706 and an internal memory 707, the nonvolatile storage medium 706 stores an operating system, a computer program, and a database, the internal memory 707 provides an environment for the operating system and the computer program in the nonvolatile storage medium to run, and when the processor 702 executes the computer program stored in the memory, the internet test bed topology fidelity evaluation method of embodiment 1 above is implemented, as follows:

inputting a topology generation tool into a domain of discourse of the Internet test bed topology simulation system;

calculating the importance weight value of each topological feature in the domain relative to the test index;

sorting the importance of all topological features in the domain of discourse according to the importance weight value, and outputting a corresponding importance sorting vector;

computing the kth topological feature f_kPyramid model parameter p ═ p relative to parallel features_s+k₁·l_pAnd a first equivalent deduction value of the test index x;

for any one quadruple (p)_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Generating a topological graph G (p) by adopting a parallel characteristic pyramid model_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) (ii) a Wherein,k₂＝0,1,L,m_q，k₄＝0,1,L,m_n，p_dand delta_dIs a preset parameter;

computing the kth topological feature f_kPyramid model parameter q ═ q relative to parallel features_s+k₂·l_qAnd testing a second equivalent derived metric value of the index x;

for the kth topological feature in the theoretical domain, calculating the topological feature in a topological graph G (p)_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Topology ofCharacteristic value f_k(p_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n)；

Computing the kth topological feature f_kPyramid model parameter delta relative to parallel features_s+k₃·l_δA third spatial recognition capability value of (a);

computing the kth topological feature f_kParameter delta is equal to delta relative to parallel characteristic pyramid model_s+k₃·l_δAnd a third equivalent deduction value of the test index x;

calculating the kth topological feature f according to the first equivalent deduction degree value, the second equivalent deduction degree value and the third equivalent deduction degree value_kThe integrated equivalent derived metric value of (a);

calculating the kth topological feature f according to the comprehensive space recognition capability value and the comprehensive equivalent deduction degree value_kAn importance weight value relative to the test index x;

Example 4:

the present embodiment provides a storage medium, which is a computer-readable storage medium, and stores a computer program, and when the computer program is executed by a processor, the method for evaluating the topology fidelity of the internet test bed in the foregoing embodiment 1 is implemented as follows:

for the kth topological feature in the theory domain, the topological feature is calculated in a topological graph G (p)_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Topological characteristic value f of_k(p_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n)；

Computing the kth topological feature f_kRelative to the parameter q of the parallel characteristic pyramid model, q is q_s+k₂·l_qA second spatial recognition capability value of (a);

for the kth topological feature in the theory domain, the topological feature is calculated in a topological graph G (p)_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Topological characteristic value f of_k(p_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n)；

computing the kth topological feature f_kPyramid model parameter delta relative to parallel features_s+k₃·l_δAnd a third equivalent deduction degree value of the test index x;

The storage medium in this embodiment may be a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a Random Access Memory (RAM), a usb disk, a removable hard disk, or other media.

In summary, the invention provides a quantitative internet test bed topological feature importance ranking method in view of infinite characteristics of the topological structure of the internet test bed, overcomes the defects of strong subjectivity of traditional topological feature extraction and configuration and lack of quantitative data support, takes equivalent deduction of specific test indexes as a standard, can realize self-adaptive matching of topological fidelity optimal tools from different topological generation tools according to different test indexes, and provides technical support for accurate testing of internet technologies which strongly depend on the topological structure, such as resource positioning, admission control, routing protocols, active defense and the like.

The above description is only for the preferred embodiments of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive concept of the present invention within the scope of the present invention.

Claims

1. An internet test bed topology fidelity evaluation method is characterized by comprising the following steps:

calculating an importance weight value of each topological feature in the domain of discourse relative to the test index, sorting the importance of all the topological features in the domain of discourse, and outputting a corresponding importance sorting vector;

extracting the first n topological features in the importance ranking according to the importance ranking vector, and generating a cost function;

evaluating the topology fidelity of the topology generation tool by adopting a cost function;

the method includes the steps of calculating an importance weight value of each topological feature in a domain relative to a test index, sorting the importance of all topological features in the domain, and outputting a corresponding importance sorting vector, and specifically includes:

computing the kth topological feature f_kRelative to flatLine characteristic pyramid model parameter delta is delta_s+k₃·l_δAnd a third equivalent deduction value of the test index x;

wherein (p)_s,l_p,m_p)＝(0.2,0.1,5)，(q_s,l_q,m_q)＝(0.1,0.1,5)，(δ_s,l_δ,m_δ)＝(0.008,0.02,5)，(n_s,l_n,m_n)＝(1000,500,11)。

2. The method of claim 1, wherein the computing of the kth topological feature f_kWith respect to the parameter p ═ p_s+k₁·l_pA first spatial recognition capability value of (a), as follows:

wherein,

the k topological feature f is calculated_kPyramid model parameter p ═ p relative to parallel features_s+k₁·l_pAnd a first of the test index xThe equivalent derived metric value is given by:

wherein,

wherein,

wherein,

the k topological feature f is calculated_kParameter delta is equal to delta relative to parallel characteristic pyramid model_s+k₃·l_δThe third spatial recognition capability value of (1), as follows:

wherein,

the k topological feature f is calculated_kPyramid model parameter delta relative to parallel features_s+k₃·l_δAnd a third equivalent derivative value for test index x, as follows:

wherein,

3. the evaluation method according to claim 1, wherein the k-th topological feature f is calculated according to the first space recognition capability value, the second space recognition capability value and the third space recognition capability value_kThe comprehensive space recognition capability value of (1) is as follows:

wherein,

4. The evaluation method according to claim 1, wherein the k-th topology bit is calculated based on the first equivalent derived metric value, the second equivalent derived metric value and the third equivalent derived metric valueSign f_kThe integrated equivalent derived measure value of (a) is as follows:

wherein,

5. The evaluation method according to claim 1, wherein the kth topological feature f is calculated according to the comprehensive space recognition capability value and the comprehensive equivalent deduction degree value_kThe importance weight value relative to test index x is given by:

R_x(f_k)＝w_s·S_r(f_k)+w_e·E_x(f_k)

wherein, w_s＝0.5，w_e＝0.5，S_r(f_k) Identifying the capability value for the synthesis space, E_x(f_k) Is the comprehensive equivalent deductive degree value.

6. The evaluation method according to any one of claims 1 to 5, wherein the topological feature comprises an average degree of nodes f₁Maximum nucleus f₂Average path length f₃Average clustering coefficient f₄Mixed coordination coefficient f₅Algebraic connectivity f₆Natural connectivity f₇Weighted spectral distribution f₈Characteristic value 1 repetition degree f₉Laplacian spectrum radius f₁₀Adjacent spectral radius f₁₁And the radius f of the regular Laplacian spectrum₁₂。

7. An internet test bed topology fidelity assessment device, the device comprising:

the second input module is used for inputting the topological characteristics and the corresponding calculation method into the domain of the topology simulation system of the Internet test bed; wherein, the discourse domain is a discourse domain oriented to the equivalent deduction of the test index;

the evaluation module is used for evaluating the topology fidelity of the topology generation tool by adopting a value function;

the calculation module specifically includes:

a first generation unit for generating a first four-tuple (p) for any one of the four-tuples_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Generating a topological graph G (p) by adopting a parallel characteristic pyramid model_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) (ii) a Wherein k is₁＝0,1,L,m_p，k₄＝0,1,L,m_n，q_dAnd delta_dIs a preset parameter;

a first calculation unit for calculating the k-th topological feature in the theoretical domain in the topological graph G (p)_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Topological characteristic value f of_k(p_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n)；

A first test unit for employing the topology G (p)_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n) Configuring the topological connection relation between simulation nodes on the Internet test bed, and testing to obtain the value x (p) of the test index x_s+k₁·l_p,q_d,δ_d,n_s+k₄·l_n)；

A second calculation unit for calculating the kth topological feature f_kPyramid model parameter p ═ p relative to parallel features_s+k₁·l_pA first spatial recognition capability value of (a);

a third calculation unit for calculating the kth topological feature f_kPyramid model parameter p ═ p relative to parallel features_s+k₁·l_pAnd a first equivalent deduction value of the test index x;

a second generation unit for generating a second vector for any one of the four tuples (p)_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Generating a topological graph G (p) by adopting a parallel characteristic pyramid model_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) (ii) a Wherein k is₂＝0,1,L,m_q，k₄＝0,1,L,m_n，p_dAnd delta_dIs a preset parameter;

a fourth calculating unit, for calculating the kth topological feature in the theoretical domain, the topological feature is in the topological graph G (p)_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Topological characteristic value f of_k(p_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n)；

A second test unit for employing the topology G (p)_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n) Configuration of simulation nodes on Internet test bedThe topological connection relation between the two is tested to obtain the value x (p) of the test index x_d,q_s+k₂·l_q,δ_d,n_s+k₄·l_n)；

A fifth calculation unit for calculating the kth topological feature f_kPyramid model parameter q ═ q relative to parallel features_s+k₂·l_qA second spatial recognition capability value of (a);

a sixth calculation unit for calculating the kth topological feature f_kPyramid model parameter q ═ q relative to parallel features_s+k₂·l_qAnd a second equivalent derived measure value for the test index x;

a third generating unit for generating a fourth tuple (p) for any one of the four tuples_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Generating a topological graph by adopting a parallel characteristic pyramid model; wherein k is₃＝0,1,L,m_δ，k₄＝0,1,L,m_n，p_dAnd q is_dIs a preset parameter;

a seventh calculating unit, for calculating the kth topological feature in the theory domain, the topological feature is in the topological graph G (p)_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Topological characteristic value f of_k(p_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n)；

A third test unit for employing the topology G (p)_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n) Configuring the topological connection relation between simulation nodes on the Internet test bed, and testing to obtain the value x (p) of the test index x_d,q_d,δ_s+k₃·l_δ,n_s+k₄·l_n)；

An eighth calculation unit for calculating the kth topological feature f_kPyramid model parameter delta relative to parallel features_s+k₃·l_δA third spatial recognition capability value of (a);

a ninth calculating unit for calculating the kth topological feature f_kPyramid model parameter delta relative to parallel features_s+k₃·l_δAnd a third equivalent deduction degree value of the test index x;

a tenth calculating unit, configured to calculate the kth topological feature f according to the first space recognition capability value, the second space recognition capability value, and the third space recognition capability value_kThe comprehensive space recognition capability value;

an eleventh calculating unit, configured to calculate a kth topological feature f according to the first equivalent derived metric value, the second equivalent derived metric value, and the third equivalent derived metric value_kThe integrated equivalent derived metric value of (2);

a twelfth calculating unit, configured to calculate the kth topological feature f according to the comprehensive space recognition capability value and the comprehensive equivalent deduction degree value_kAn importance weight value relative to the test index x;

the output unit is used for sorting the importance of all the topological features in the domain of discourse according to the importance weight value and outputting corresponding importance sorting vectors;

8. A computer device comprising a processor and a memory for storing a program executable by the processor, wherein the processor, when executing the program stored in the memory, implements the internet test bed topology fidelity assessment method of any of claims 1-6.

9. A storage medium storing a program, wherein the program, when executed by a processor, implements the internet test bed topology fidelity evaluation method of any of claims 1-6.