Network reliability evaluation method and apparatus
By constructing an IP network state sample dataset and performing maximum traffic calculation and depth-first search, combined with Monte Carlo simulation, the efficiency problem of IP network reliability assessment is solved, enabling efficient assessment of IP network reliability within limited resources and time.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA TELECOM CORP LTD
- Filing Date
- 2022-03-28
- Publication Date
- 2026-06-02
AI Technical Summary
Existing technologies struggle to efficiently assess the reliability of two-way connectivity in IP networks within limited time and resources, especially due to the NP-hard problem of solving the network's polymorphic minimum path set vector or polymorphic minimum cut set vector, which leads to decreased algorithm efficiency.
By constructing a state sample dataset of the IP network, the maximum network traffic is calculated, and a hierarchical network is constructed using depth-first search. Combined with Monte Carlo simulation, the network reliability is evaluated. The maximum network traffic is used as the evaluation criterion, and the number of successful simulations is updated until the preset number of simulations is reached.
It enables efficient evaluation of IP network reliability within limited resources and time, solves the problem of excessive computational resource and time consumption, and improves the efficiency of network reliability evaluation.
Smart Images
Figure CN116866175B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of network technology, and in particular to a method and apparatus for network reliability assessment. Background Technology
[0002] Network reliability, as one of the indicators for evaluating network capabilities, can effectively measure the quality of network services, promote network improvement or optimization, thereby reducing various losses caused by network failures and improving network efficiency and effectiveness.
[0003] As a carrier of data communication, IP networks require highly reliable transmission of communication information to meet end-to-end service quality requirements. Reliability research on IP networks primarily involves calculating the probability of successful communication of specified demand traffic between end-to-end nodes, expressed as a digital probability representation.
[0004] Due to the large scale and complex structure of the network, it is difficult to obtain accurate reliability values using mathematical models. Therefore, the evaluation of the reliability of the two-way connectivity of the network is mainly done by comparing the network state vector with the pre-solved network polymorphic minimum path set vector or polymorphic minimum cut set vector. If all elements of the network state vector are greater than or equal to all elements of the polymorphic minimum path set vector, it indicates that the network state vector constructed at this time can successfully transmit the required traffic from the source node to the target node. Summary of the Invention
[0005] The inventors noted that because IP networks consist of hundreds of network components, and research has shown that finding all polymorphic minimum path set vectors or polymorphic minimum cut set vectors is a nondeterministic (NP) problem of polynomial complexity, the algorithm's efficiency drops sharply as the network size increases, making it difficult to solve for the two-way connectivity reliability of IP networks within a finite time. Furthermore, as the network size increases, the computational resources required to find all polymorphic minimum path set vectors or polymorphic minimum cut set vectors also gradually increase, making efficient network reliability assessment impossible.
[0006] Accordingly, this disclosure provides a network reliability assessment scheme that can efficiently assess network reliability without consuming excessive resources.
[0007] According to a first aspect of the present disclosure, a network reliability assessment method is provided, comprising: constructing a state sample dataset of each component in an IP network based on a state and probability table of each component and a preset number of simulations; performing a network reliability assessment, wherein performing the network reliability assessment includes: constructing a network state vector based on the state sample dataset of each component; calculating the maximum network traffic of the IP network under the network state vector; updating the number of successful network state assessments if the maximum network traffic is not less than a preset demand traffic, otherwise not updating the number of successful network state assessments; repeating the network reliability assessment until the number of network reliability assessments reaches the preset number of simulations; and determining the reliability of the IP network based on the number of successful network state assessments and the preset number of simulations.
[0008] In some embodiments, calculating the maximum network traffic of the IP network under the network state vector includes: for the i-th component in the IP network, setting the current traffic flowing through the i-th component to a preset initial value, where 1≤i≤I, and I is the total number of components; constructing a residual network of the IP network based on the current traffic of the i-th component; searching the residual network to construct a hierarchical network; if the target node is included in the hierarchical network, searching for a path from the source node to the target node in the hierarchical network, updating the current traffic, and repeatedly constructing the residual network of the IP network based on the current traffic of the i-th component; if the target node is not included in the hierarchical network, using the current traffic as a candidate maximum network traffic; and using the maximum value among the candidate maximum network traffic as the maximum network traffic of the IP network.
[0009] In some embodiments, searching the residual network includes performing a depth-first search on the residual network to construct a hierarchical network, wherein the hierarchical network is a layered graph of the shortest paths from the source node to all nodes except the source node.
[0010] In some embodiments, searching for a path from the source node to the target node in the hierarchical network includes performing a depth-first search in the hierarchical network to find a path from the source node to the target node.
[0011] In some embodiments, constructing the network state vector includes randomly sampling from the state sample dataset of each component in the IP network to construct the network state vector.
[0012] In some embodiments, constructing the state sample dataset of each component in the IP network includes: based on the j-th state b of the i-th component in the IP network. ijThe probability value p ij The j-th state b of the i-th component is determined by the preset number of simulations M. ij The number of samples, where 1≤i≤I, 1≤j≤J, where I is the total number of components and J is the total number of states; the sample counts of each state of the i-th component in the IP network are merged to construct the state sample dataset of the i-th component in the IP network.
[0013] In some embodiments, the j-th state b of the i-th component ij The number of samples is the j-th state b of the i-th component. ij The probability value p ij The product of the preset number of simulations M.
[0014] In some embodiments, the preset number of simulations Where the parameter j0 satisfies
[0015]
[0016] Where ε is the simulation error and δ is the expected evaluation accuracy.
[0017] In some embodiments, the reliability of the IP network is the quotient of the number of successful network states and the preset number of simulations.
[0018] According to a second aspect of the present disclosure, a network reliability assessment apparatus is provided, comprising: a first processing module configured to construct a state sample dataset of each component in an IP network based on a state and probability table of each component in the IP network and a preset number of simulations; a second processing module configured to perform network reliability assessment, wherein performing network reliability assessment includes: constructing a network state vector based on the state sample dataset of each component; calculating the maximum network traffic of the IP network under the network state vector; updating the number of successful network state assessments if the maximum network traffic is not less than a preset demand traffic, otherwise not updating the number of successful network state assessments; repeating the network reliability assessment until the number of network reliability assessments reaches the preset number of simulations; and a third processing module configured to determine the reliability of the IP network based on the number of successful network state assessments and the preset number of simulations.
[0019] In some embodiments, the second processing module is configured to, for the i-th component in the IP network, set the current traffic flowing through the i-th component to a preset initial value, where 1≤i≤I, and I is the total number of components; construct a residual network of the IP network based on the current traffic; search the residual network to construct a hierarchical network; if the target node is included in the hierarchical network, search for a path from the source node to the target node in the hierarchical network; update the current traffic; and repeatedly construct the residual network of the IP network based on the current traffic; if the target node is not included in the hierarchical network, use the current traffic as the candidate network maximum traffic, and use the maximum value among the candidate network maximum traffic as the network maximum traffic of the IP network.
[0020] In some embodiments, the second processing module is configured to perform a depth-first search on the residual network to construct a hierarchical network, wherein the hierarchical network is a layered graph of the shortest paths from the source node to all nodes except the source node.
[0021] In some embodiments, the second processing module is configured to perform a depth-first search in the hierarchical network to find a path from the source node to the target node.
[0022] In some embodiments, the second processing module is configured to randomly sample the state sample dataset of each component in the IP network to construct a network state vector.
[0023] In some embodiments, the first processing module is configured to process the j-th state b of the i-th component in the IP network. ij The probability value p ij The j-th state b of the i-th component is determined by the preset number of simulations M. ij The number of samples, where 1≤i≤I, 1≤j≤J, where I is the total number of components and J is the total number of states, is used to merge the sample counts of each state of the i-th component in the IP network to construct the state sample dataset of the i-th component in the IP network.
[0024] In some embodiments, the j-th state b of the i-th component ij The number of samples is the j-th state b of the i-th component. ij The probability value p ij The product of the preset number of simulations M.
[0025] In some embodiments, the preset number of simulations Where the parameter j0 satisfies
[0026]
[0027] Where ε is the simulation error and δ is the expected evaluation accuracy.
[0028] In some embodiments, the reliability of the IP network is the quotient of the number of successful network states and the preset number of simulations.
[0029] According to a third aspect of the present disclosure, a network reliability assessment apparatus is provided, comprising: a memory configured to store instructions; and a processor coupled to the memory, the processor being configured to execute instructions stored in the memory to implement the method as described in any of the above embodiments.
[0030] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method as described in any of the above embodiments.
[0031] Other features and advantages of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description
[0032] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0033] Figure 1 This is a flowchart illustrating a network reliability assessment method according to an embodiment of the present disclosure;
[0034] Figure 2 This is a schematic diagram of the structure of a network reliability evaluation device according to an embodiment of the present disclosure;
[0035] Figure 3 This is a schematic diagram of the structure of a network reliability assessment device according to another embodiment of the present disclosure. Detailed Implementation
[0036] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.
[0037] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of this disclosure.
[0038] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.
[0039] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.
[0040] In all examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.
[0041] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.
[0042] Figure 1 This is a schematic flowchart of a network reliability assessment method according to an embodiment of the present disclosure. In some embodiments, the following network reliability assessment method is performed by a network reliability assessment apparatus.
[0043] In step 101, a state sample dataset of each component in the IP network is constructed based on the state and probability table of each component in the IP network and the preset number of simulations.
[0044] In some embodiments, based on the j-th state b of the i-th component in the IP network ij The probability value p ij The j-th state b of the i-th component is determined by the preset number of simulations M. ij The number of samples, where 1≤i≤I, 1≤j≤J, where I is the total number of components and J is the total number of states.
[0045] For example, the j-th state b of the i-th component ij The number of samples is the j-th state b of the i-th component. ij The probability value p ij The product of the number of simulations M and the preset number of simulations.
[0046] Next, the sample counts of each state of the i-th component in the IP network are merged to construct the state sample dataset of the i-th component in the IP network.
[0047] For example, the state sample dataset of the i-th component. iAs shown in formula (1).
[0048] sample i =[{b i1 ,}·p i1 ×M,{b i2 ,}·p i2 ×M,…,{b iJ ,}·p iJ ×M] (1)
[0049] In some embodiments, the number of simulations is preset. The parameter j0 is shown in formula (2).
[0050]
[0051] Where ε is the simulation error and δ is the expected evaluation accuracy.
[0052] In step 102, a network reliability assessment is performed, which includes:
[0053] 1) Construct the network state vector based on the state sample dataset of each component.
[0054] In some embodiments, random sampling is performed on the state sample dataset of each component in the IP network to construct the network state vector X. s ={x1,x2,…,x |A|}
[0055] 2) Calculate the maximum network traffic of the IP network under the network state vector.
[0056] To facilitate solving for the maximum flow in an IP network, we assume the IP network is represented by G = (V, E, s, t), where V represents the set of nodes, E represents the set of links, s represents the source node for evaluating the reliability of two-way connectivity, and t represents the target node for evaluating the reliability of two-way connectivity. We also let A = {V∪E} represent the set of nodes and links in the IP network, collectively called the set of IP network components, where i ∈ A. Let c(i) represent the capacity of component i, and we have c(i) = x i .
[0057] In some embodiments, the step of calculating the maximum network traffic of the IP network under the network state vector includes:
[0058] a) For the i-th component in the IP network, set the current flow f(i) through the i-th component to a preset initial value, where 1≤i≤I and I is the total number of components.
[0059] For example, f(i) = 0.
[0060] b) Construct the residual network G(f(i)) of the IP network based on the current traffic of the i-th component.
[0061] c) Search the residual network G(f(i)) to construct the hierarchical network AG(f(i)).
[0062] In some embodiments, perform a depth-first search on the residual network G(f(i)) to construct the hierarchical network AG(f(i)), where the hierarchical network AG(f(i)) is a layered graph of the shortest paths from the source node to all nodes except the source node. Paths with the same distance are in the same layer.
[0063] d) Determine whether the target node is included in the hierarchical network AG(f(i)).
[0064] If the target node is included in the hierarchical network AG(f(i)), then execute step e); if the target node is not included in the hierarchical network, then execute step f).
[0065] e) Search for a path from the source node to the target node in the hierarchical network, update the current traffic according to the searched path, and repeat step b).
[0066] In some embodiments, perform a depth-first search in the hierarchical network to search for a path from the source node to the target node.
[0067] f) Take the current traffic as the candidate network maximum traffic.
[0068] g) Take the maximum value in the candidate network maximum traffic as the network maximum traffic of the IP network.
[0069] 3) If the network maximum traffic is not less than the preset required traffic, update the network status success count; otherwise, do not update the network status success count.
[0070] For example, let the required traffic be d. If the network maximum traffic V(X s ) ≥ d, it indicates that the network status vector of this simulation can successfully transmit the required traffic d from the source node to the target node. At this time, update the network status success count, that is, T = T + 1. If V(X s ) < d, it indicates that the network status vector of this simulation cannot successfully transmit the required traffic d from the source node to the target node. At this time, there is no need to update the network status success count.
[0071] In step 103, repeat the network reliability assessment until the number of network reliability assessment times reaches the preset simulation times.
[0072] In step 104, determine the reliability of the IP network according to the network status success count and the preset simulation times.
[0073] In some embodiments, the reliability R of the IP network is the quotient of the number of successful network states T and the preset number of simulations M, i.e., R = T / M.
[0074] In the network reliability assessment method provided in the above embodiments of this disclosure, network reliability assessment can be performed efficiently without consuming a lot of resources by using network reliability assessment based on maximum network traffic (i.e., improved Monte Carlo simulation).
[0075] Figure 2 This is a schematic diagram of the structure of a network reliability evaluation device according to an embodiment of this disclosure. Figure 2 As shown, the network reliability assessment device includes a first processing module 21, a second processing module 22, and a third processing module 23.
[0076] The first processing module 21 is configured to construct a state sample dataset of each component in the IP network based on the state and probability table of each component in the IP network and a preset number of simulations.
[0077] In some embodiments, the first processing module 21 determines the j-th state b of the i-th component in the IP network. ij The probability value p ij The j-th state b of the i-th component is determined by the preset number of simulations M. ij The number of samples is denoted as 1≤i≤I, 1≤j≤J, where I is the total number of components and J is the total number of states. The sample counts of each state of the i-th component in the IP network are merged to construct the state sample dataset of the i-th component in the IP network.
[0078] For example, the j-th state b of the i-th component ij The number of samples is the j-th state b of the i-th component. ij The probability value p ij The product of the number of simulations M and the preset number of simulations.
[0079] In some embodiments, the number of simulations is preset. The parameter j0 satisfies the above formula (2).
[0080] The second processing module 22 is configured to perform network reliability assessment, which includes: constructing a network state vector based on the state sample dataset of each component; calculating the maximum network traffic of the IP network under the network state vector; updating the number of successful network state assessments if the maximum network traffic is not less than the preset required traffic, otherwise not updating the number of successful network state assessments; and repeating the network reliability assessment until the number of network reliability assessments reaches the preset number of simulations.
[0081] In some embodiments, the second processing module 22 sets the current traffic flowing through the i-th component in the IP network to a preset initial value, where 1≤i≤I, and I is the total number of components. Based on the current traffic, it constructs a residual network of the IP network and searches the residual network to construct a hierarchical network. If the target node is included in the hierarchical network, it searches for a path from the source node to the target node in the hierarchical network, updates the current traffic, and repeatedly constructs the residual network of the IP network based on the current traffic. If the target node is not included in the hierarchical network, it uses the current traffic as the candidate network maximum traffic and uses the maximum value among the candidate network maximum traffic as the network maximum traffic of the IP network.
[0082] For example, the second processing module 22 performs a depth-first search on the residual network to construct a hierarchical network, wherein the hierarchical network is a layered graph of the shortest paths from the source node to all nodes except the source node.
[0083] For example, the second processing module 22 performs a depth-first search in the hierarchical network to find a path from the source node to the target node.
[0084] For example, the second processing module 22 randomly samples the state sample dataset of each component in the IP network to construct a network state vector.
[0085] The third processing module 23 is configured to determine the reliability of the IP network based on the number of successful network status tests and the preset number of simulation tests.
[0086] In some embodiments, the reliability of the IP network is the quotient of the number of successful network state operations and the preset number of simulations.
[0087] Figure 3 This is a schematic diagram of the structure of a network reliability evaluation apparatus according to another embodiment of this disclosure. Figure 3 As shown, the network reliability assessment device includes a memory 31 and a processor 32.
[0088] Memory 31 is used to store instructions, and processor 32 is coupled to memory 31. Processor 32 is configured to execute instructions based on memory storage, as shown in the example. Figure 1 The method involved in any of the embodiments.
[0089] like Figure 3 As shown, the network reliability assessment device also includes a communication interface 33 for exchanging information with other devices. Additionally, the device includes a bus 34, through which the processor 32, communication interface 33, and memory 31 communicate with each other.
[0090] The memory 31 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device. The memory 31 may also be a memory array. The memory 31 may also be divided into blocks, and the blocks may be combined into virtual volumes according to certain rules.
[0091] Furthermore, processor 32 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present disclosure.
[0092] This disclosure also relates to a computer-readable storage medium storing computer instructions that, when executed by a processor, implement... Figure 1 The method involved in any of the embodiments.
[0093] This disclosure, by selecting the maximum network traffic as the basis for evaluating the network state vector, enables an effective assessment of the reliability of two-way connectivity in IP networks within acceptable computation time and resources. It solves the problems of long computation time and high computational resources faced by Monte Carlo simulation methods based on polymorphic minimum path set vectors or polymorphic minimum cut set vectors.
[0094] In some embodiments, the functional units described above may be implemented as general-purpose processors, programmable logic controllers (PLCs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or any suitable combination thereof for performing the functions described herein.
[0095] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.
[0096] The description in this disclosure is provided for illustrative and descriptive purposes only and is not intended to be exhaustive or to limit the disclosure to its forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of this disclosure and to enable those skilled in the art to understand this disclosure and to design various embodiments with various modifications suitable for a particular purpose.
Claims
1. A network reliability assessment method, comprising: Based on the state and probability tables of each component in the IP network, and a preset number of simulations, a state sample dataset of each component in the IP network is constructed. Performing a network reliability assessment, wherein performing the network reliability assessment includes: Construct a network state vector based on the state sample dataset of each component; Calculate the maximum network traffic of the IP network under the network state vector; If the maximum network traffic is not less than the preset required traffic, then update the network status success count; otherwise, do not update the network status success count. Repeat the network reliability assessment until the preset number of simulations is reached; The reliability of the IP network is determined based on the number of successful network status tests and the preset number of simulation tests. The calculation of the maximum network traffic of the IP network under the network state vector includes: For the i-th component in the IP network, the current traffic flowing through the i-th component is set to a preset initial value, where I represents the total number of components; Construct the residual network of the IP network based on the current traffic of the i-th component; The residual network is searched to construct a hierarchical network; If the target node is included in the hierarchical network, a path from the source node to the target node is searched in the hierarchical network, the current traffic is updated, and the residual network of the IP network is repeatedly constructed based on the current traffic of the i-th component. If the target node is not included in the hierarchical network, then the current traffic is taken as the candidate network maximum traffic; The maximum value among the candidate network maximum traffic is taken as the network maximum traffic of the IP network.
2. The method according to claim 1, wherein, The search of the residual network includes: A depth-first search is performed on the residual network to construct a hierarchical network, wherein the hierarchical network is a layered graph of the shortest paths from the source node to all nodes except the source node.
3. The method according to claim 2, wherein, The step of searching for a path from the source node to the target node in the hierarchical network includes: A depth-first search is performed in the hierarchical network to find a path from the source node to the target node.
4. The method according to claim 1, wherein, The constructed network state vector includes: Random sampling is performed on the state sample dataset of each component in the IP network to construct the network state vector.
5. The method according to claim 1, wherein, The state sample dataset for each component in the IP network includes: Based on the j-th state of the i-th component in the IP network probability value and the preset number of simulations Determine the j-th state of the i-th component. The number of samples, of which J represents the total number of states; The sample counts of each state of the i-th component in the IP network are merged to construct the state sample dataset of the i-th component in the IP network.
6. The method according to claim 5, wherein, The j-th state of the i-th component The number of samples is the j-th state of the i-th component. probability value With the preset number of simulations The product of.
7. The method according to claim 1, wherein, The preset number of simulations , where parameters satisfy in To simulate error, To achieve the desired evaluation accuracy.
8. The method according to any one of claims 1-7, wherein, The reliability of the IP network is the quotient of the number of successful network states and the preset number of simulations.
9. A network reliability assessment device, comprising: The first processing module is configured to construct a state sample dataset of each component in the IP network based on the state and probability table of each component in the IP network and a preset number of simulations. The second processing module is configured to perform network reliability assessment, wherein the network reliability assessment includes: constructing a network state vector based on the state sample dataset of each component; calculating the maximum network traffic of the IP network under the network state vector; updating the network state success count if the maximum network traffic is not less than a preset required traffic, otherwise not updating the network state success count; repeating the network reliability assessment until the number of network reliability assessments reaches the preset number of simulations; wherein for the i-th component in the IP network, the current traffic flowing through the i-th component is set to a preset initial value, wherein... I represents the total number of components. Based on the current traffic, a residual network of the IP network is constructed. The residual network is searched to construct a hierarchical network. If the target node is included in the hierarchical network, a path from the source node to the target node is searched in the hierarchical network. The current traffic is updated, and the residual network of the IP network is constructed again based on the current traffic. If the target node is not included in the hierarchical network, the current traffic is used as the candidate network maximum traffic, and the maximum value among the candidate network maximum traffic is used as the network maximum traffic of the IP network. The third processing module is configured to determine the reliability of the IP network based on the number of successful network status tests and the preset number of simulation tests.
10. The apparatus according to claim 9, wherein, The second processing module is configured to perform a depth-first search on the residual network to construct a hierarchical network, wherein the hierarchical network is a layered graph of the shortest paths from the source node to all nodes except the source node.
11. The apparatus according to claim 10, wherein, The second processing module is configured to perform a depth-first search in the hierarchical network to find a path from the source node to the target node.
12. The apparatus according to claim 9, wherein, The second processing module is configured to randomly sample the state sample dataset of each component in the IP network to construct a network state vector.
13. The apparatus according to claim 9, wherein, The first processing module is configured to process the j-th state of the i-th component in the IP network. probability value and the preset number of simulations Determine the j-th state of the i-th component. The number of samples, of which J represents the total number of states. The sample counts of each state of the i-th component in the IP network are merged to construct the state sample dataset of the i-th component in the IP network.
14. The apparatus according to claim 13, wherein, The j-th state of the i-th component The number of samples is the j-th state of the i-th component. probability value With the preset number of simulations The product of.
15. The apparatus according to claim 9, wherein, The preset number of simulations , where parameters satisfy in To simulate error, To achieve the desired evaluation accuracy.
16. The apparatus according to any one of claims 9-15, wherein, The reliability of the IP network is the quotient of the number of successful network states and the preset number of simulations.
17. A network reliability assessment device, comprising: The memory is configured to store instructions; A processor, coupled to memory, configured to implement the method as described in any one of claims 1-8 based on memory-stored instruction execution.
18. A computer-readable storage medium, wherein, A computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method as described in any one of claims 1-8.