Network link availability evaluation method and device, electronic equipment and storage medium

Abstract

The present disclosure relates to a link availability evaluation method and device in a network, an electronic device and a storage medium, the method comprising: obtaining network configuration information, traffic demand information and fault scenario information of a target network to be evaluated; determining a key link non-overload probability under each fault scenario according to the network configuration information, the traffic demand information and the fault scenario information, the key link non-overload probability under each fault scenario representing a probability that a link through which key traffic passes does not overload under each fault scenario; and determining a link availability evaluation result corresponding to the target network according to the key link non-overload probability under each fault scenario and an occurrence probability corresponding to each fault scenario, the link availability evaluation result representing a probability that a link through which key traffic passes does not overload under any fault scenario in the target network. Thus, it can be scientifically and effectively evaluated whether a link on a key traffic path can not overload under various fault scenarios.

Description

Technical Field

[0001] This disclosure relates to the field of computer networks, and more particularly to a method and apparatus for evaluating link availability in a network, as well as electronic devices and storage media. Background Technology

[0002] With the development of the internet, internet service providers face numerous challenges. To meet the ever-increasing demands and expectations of users, they need to improve service quality. To this end, service providers have adopted various measures, including but not limited to customizing network solutions for different customer groups.

[0003] For users, better and more stable network availability is a crucial consideration when choosing a service provider. Users need to enjoy stable and fast network services at all times. Therefore, service providers must ensure that their services meet these needs to maintain competitiveness and market share.

[0004] To ensure the quality of network services for critical customers, network administrators need to monitor their traffic in real time to ensure that network transmission is not affected by bottlenecks. To this end, administrators need to implement a series of strategies, such as network traffic monitoring, routing policy optimization, and traffic engineering deployment, to ensure unimpeded critical traffic paths. Furthermore, rigorous testing and verification processes are essential to ensure the effectiveness of the measures taken.

[0005] Service Providers (ISPs) and customers typically enter into Service Level Agreements (SLAs) that clearly define key performance indicators (KPIs) such as service level, service quality, and network availability. These agreements not only guarantee service stability and security but also stipulate important terms such as fault recovery time, providing customers with clear service assurances. Within the industry, the "five nines" standard (99.999% uptime) is widely adopted, meaning that downtime should not exceed 5.26 minutes per year. To meet this high standard, network administrators must assess the availability of links along critical customer traffic paths to ensure they comply with the agreement requirements.

[0006] In recent years, significant progress has been made in research to assist network administrators in assessing network availability. This research primarily focuses on two major areas: the data plane and the control plane. Data plane verification tools use Boolean variables to determine network traffic reachability or link overload, but they are only applicable to single-traffic scenarios and simplify the problem to a binary decision (yes or no), failing to accurately assess the probability and severity of overload, which is clearly insufficient for practical applications. Control plane verification tools, on the other hand, actively detect reachability violations by extracting data plane models from configuration files. However, these tools cannot accurately simulate traffic load distribution or perform probabilistic analysis of fault scenarios. Furthermore, some studies utilize traffic distribution maps to analyze load attributes, but lack probabilistic reasoning capabilities for faults. Other studies assess network availability under probabilistic fault scenarios, attempting to improve existing methods, but most of these solutions are not applicable to multi-flow scenarios, and their brute-force-based fault analysis methods limit their application in small networks.

[0007] To date, no research has been able to simultaneously consider link failures and quantify the availability of links on critical traffic paths (i.e., the probability that a link will not be overloaded). This reflects that, despite network administrators and service providers taking various measures to improve network service quality, they still lack a reliable method to measure and ensure the availability of links on critical traffic paths. Summary of the Invention

[0008] In view of this, this disclosure proposes a method and apparatus, electronic device and storage medium for evaluating the availability of links in a network, which can scientifically and effectively assess the probability that links on critical traffic paths will not be overloaded under various failure scenarios, thereby providing stronger support for ISPs and network administrators to ensure the high availability of network services.

[0009] According to one aspect of this disclosure, a method for evaluating link availability in a network is provided, comprising: acquiring network configuration information, traffic demand information, and fault scenario information of a target network to be evaluated, wherein the network configuration information includes the capacity and weight of each link in the target network, the traffic demand information includes the expected set of traffic in the target network and the traffic size corresponding to each traffic in the traffic set, and the fault scenario information includes the expected set of fault scenarios in the target network and the links that fail in each fault scenario in the fault scenario set; determining, based on the network configuration information, the traffic demand information, and the fault scenario information, the probability that a critical link is not overloaded under each fault scenario, wherein the probability that a critical link is not overloaded under each fault scenario represents the probability that the links traversed by the critical traffic in each fault scenario will not be overloaded, wherein the critical traffic is at least one traffic specified in the traffic set; and determining, based on the probability that a critical link is not overloaded under each fault scenario and the probability of occurrence corresponding to each fault scenario, a link availability evaluation result corresponding to the target network, wherein the link availability evaluation result represents the probability that the links traversed by the critical traffic in the target network will not be overloaded under any fault scenario.

[0010] In one possible implementation, determining the probability of no overload on the critical link under each fault scenario based on the network configuration information, the traffic demand information, and the fault scenario information includes: for the f-th fault scenario in the fault scenario set, determining whether the critical traffic under the f-th fault scenario can reach the destination node from the corresponding initiating node in the target network, wherein the critical traffic is at least one traffic specified in the traffic set; if the critical traffic under the f-th fault scenario cannot reach the destination node from the corresponding initiating node in the target network, determining that the probability of no overload on the critical link under the f-th fault scenario is 0; If the critical traffic in the f-th failure scenario can reach the destination node from the corresponding initiating node in the target network, the critical link set and related traffic set corresponding to the critical traffic in the f-th failure scenario are determined based on the network configuration information, the traffic demand information, and the failure scenario information; and, based on the critical link set and related traffic set corresponding to the critical traffic in the f-th failure scenario, the probability that the critical link in the f-th failure scenario is not overloaded is determined; wherein, the critical link set includes the links traversed by the critical traffic in the f-th failure scenario, and the related traffic set includes the related traffic that shares at least one link with the critical traffic in the f-th failure scenario and the traffic size of the related traffic.

[0011] In one possible implementation, determining the probability of no overload on the critical link under each fault scenario based on the network configuration information, the traffic demand information, and the fault scenario information further includes: if the critical traffic under the f-th fault scenario can reach the destination node from the corresponding initiating node in the target network, determining the critical endpoint matrix corresponding to the f-th fault scenario based on the network configuration information, the traffic demand information, and the fault scenario information, wherein the critical endpoint matrix represents the node information of the first and last nodes in the network nodes traversed by each traffic in the traffic set under the f-th fault scenario that overlap with the network nodes traversed by the critical traffic under the f-th fault scenario; the critical endpoint matrix corresponding to the f-th fault scenario and the critical endpoint matrix in the fault scenario set have already determined the critical endpoint matrix. If the critical endpoint matrix corresponding to the m-th failure scenario with a determined critical link no overload probability is equivalent, then the critical link no overload probability corresponding to the f-th failure scenario is determined as the critical link no overload probability corresponding to the m-th failure scenario. If the critical endpoint matrix corresponding to the f-th failure scenario is not equivalent to the critical endpoint matrix corresponding to the failure scenarios in the failure scenario set with determined critical link no overload probabilities, then based on the network configuration information, the traffic demand information, and the failure scenario information, the critical link set and related traffic set corresponding to the critical traffic in the f-th failure scenario are determined. Finally, based on the critical link set and related traffic set corresponding to the critical traffic in the f-th failure scenario, the critical link no overload probability in the f-th failure scenario is determined.

[0012] In one possible implementation, determining the probability that the critical links in the f-th failure scenario are not overloaded, based on the set of critical links corresponding to the critical traffic and the set of related traffic in the f-th failure scenario, includes: determining the super-volume of the set of traffic demand values ​​for which the links traversed by the critical traffic in the f-th failure scenario will not experience overload, based on the set of critical links corresponding to the critical traffic and the set of related traffic in the f-th failure scenario; obtaining the super-volume of the set of total traffic demand values ​​for the links traversed by the critical traffic in the f-th failure scenario by calculating the product of the differences between the upper and lower limits of each related traffic in the set of related traffic; and determining the ratio between the super-volume of the combined traffic demand values ​​and the super-volume of the set of total traffic demand values ​​as the probability that the critical links are not overloaded in the f-th failure scenario.

[0013] In one possible implementation, determining the hypervolume of the set of traffic demand values ​​for which the links traversed by the critical traffic in the f-th failure scenario do not experience overload, based on the set of critical links and the set of related traffic corresponding to the critical traffic in the f-th failure scenario, includes: determining the Lebesgue equation for the critical links without overload corresponding to the f-th failure scenario, based on the set of critical links and the set of related traffic corresponding to the critical traffic in the f-th failure scenario, wherein the Lebesgue equation for the critical links without overload is expressed as a value of 1 when the links traversed by the critical traffic do not experience overload, and a value of 0 when the links traversed by the critical traffic experience overload; and obtaining the hypervolume of the set of traffic demand values ​​for which the links traversed by the critical traffic in the f-th failure scenario do not experience overload by integrating the Lebesgue equation for the critical links without overload over the n-dimensional real number field, where n is the number of related traffic in the set of related traffic corresponding to the critical traffic.

[0014] In one possible implementation, the method further includes: using a Markov chain Monte Carlo algorithm to estimate the integral value of the overload-free Lebesgue equation of the critical link in the n-dimensional real domain, to obtain the hypervolume of the set of traffic demand values.

[0015] In one possible implementation, the sum of the occurrence probabilities of all fault scenarios in the fault scenario set is 1. The step of determining the link availability assessment result of the target network based on the probability of no overload on the critical link under each fault scenario and the occurrence probability of each fault scenario includes: multiplying the probability of no overload on the critical link under each fault scenario by the occurrence probability of each fault scenario and then summing them to obtain the link availability assessment result of the target network.

[0016] According to another aspect of this disclosure, a link availability assessment device in a network is provided, comprising: an acquisition module, configured to acquire network configuration information, traffic demand information, and fault scenario information of a target network to be assessed, wherein the network configuration information includes the capacity and weight of each link in the target network, the traffic demand information includes a set of expected traffic in the target network and the traffic size corresponding to each traffic in the traffic set, and the fault scenario information includes a set of expected fault scenarios in the target network and the links that fail in each fault scenario in the fault scenario set; a determination module, configured to determine, based on the network configuration information, the traffic demand information, and the fault scenario information, the probability of no overload on a critical link in each fault scenario, wherein the probability of no overload on a critical link in each fault scenario represents the probability that the links traversed by the critical traffic in each fault scenario will not be overloaded, and the critical traffic is at least one traffic specified in the traffic set; and an assessment module, configured to determine a link availability assessment result corresponding to the target network based on the probability of no overload on a critical link in each fault scenario and the probability of occurrence corresponding to each fault scenario, wherein the link availability assessment result represents the probability that the links traversed by the critical traffic in the target network will not be overloaded in any fault scenario.

[0017] According to another aspect of this disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the above-described method when executing instructions stored in the memory.

[0018] According to another aspect of this disclosure, a non-volatile computer-readable storage medium is provided that stores computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the above-described method.

[0019] According to another aspect of this disclosure, a computer program product is provided, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0020] Based on the various aspects of this disclosure, by obtaining network configuration information, traffic demand information, and fault scenario information of the target network to be evaluated, the probability that the links traversed by critical traffic in each fault scenario will not be overloaded can be determined. Combined with the probability of occurrence of each fault scenario, the availability assessment results of the target network's links without overload attributes under various fault scenarios can be effectively determined. In other words, considering link fault scenarios, the availability of any critical traffic traversed by the links in the entire target network can be effectively quantitatively assessed, or the probability of whether the links on the critical traffic path will not be overloaded under various fault scenarios can be scientifically and effectively assessed. This provides stronger support for ISPs and network administrators to ensure high availability of network services.

[0021] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0022] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0023] Figure 1a A schematic diagram of a network topology according to an embodiment of the present disclosure is shown.

[0024] Figure 1b This diagram illustrates the traffic distribution under a fault scenario according to an embodiment of the present disclosure.

[0025] Figure 2 A flowchart is shown for a link availability assessment method in a network according to an embodiment of the present disclosure.

[0026] Figure 3 A schematic diagram illustrating a workflow for link availability assessment according to an embodiment of the present disclosure is shown.

[0027] Figure 4 A block diagram of a link availability assessment apparatus in a network according to an embodiment of the present disclosure is shown.

[0028] Figure 5 A block diagram of an electronic device 1900 according to an embodiment of the present disclosure is shown. Detailed Implementation

[0029] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0030] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0031] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more of a plurality of elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C. In the description of this disclosure, "multiple" means two or more, unless otherwise explicitly specified.

[0032] It should be understood that the terms “comprising” and “including” used in the specification and claims of this disclosure indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0033] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0034] To facilitate understanding of the evaluation method proposed in the embodiments of this disclosure, this document will first combine... Figure 1a and Figure 1b The problems to be solved and the related concepts involved in the embodiments of this disclosure are explained.

[0035] like Figure 1aThe diagram illustrates a network topology that represents network nodes in a computer network and the connections between them. A, B, C, D, E, and F represent network nodes (i.e., network devices, such as routers and switches). A link (i.e., a network link) connects two directly connected network nodes via IP addresses, such as A and C. Traffic demand describes the traffic and its magnitude between any two nodes. Traffic can be characterized by the originating and destination nodes, such as traffic A→B representing the flow from originating node A to destination node B. The path indicates the nodes and links that the traffic passes through from the originating node to the destination node. For example, the path of traffic A→B includes the nodes and links that the traffic passes through from A→C→D→B. Each link has a maximum transmission rate (also known as capacity) and a weight representing its cost (this weight can be used to determine the shortest path for the traffic). A larger weight indicates a higher cost (or resource consumption) for the link. For example, the shortest path for the traffic can be selected by calculating the sum of the weights of the links in all possible paths and choosing the path with the smallest sum of weights.

[0036] Assumption Figure 1a The network shown presents two types of traffic demands: d1 represents critical customer traffic (A→B) and its volume, while d2 represents ordinary traffic (E→G) and its volume. Assume all links in the network have a capacity of 10Gbps. Under normal circumstances, A→B and E→G traffic will not share any links during transmission. To ensure that the links along the path of critical customer traffic are not overloaded (overload free), it is sufficient that d1 ≤ 10. However, if a link fails, such as link EF, the path of ordinary traffic E→G will be rerouted, for example, to E→C→D→F→G. In this case, ordinary traffic and critical traffic A→B will share a common link CD. To determine whether the link along the path of critical traffic is overloaded, a comprehensive and detailed consideration of traffic demands d1 and d2 is required.

[0037] like Figure 1b As shown, if the traffic volume of d1 is uniformly distributed within the range [3, 12] and the traffic volume of d2 is uniformly distributed within the range [5, 8], and since the link capacity is 10Gbps, the probability of overload occurring on the critical customer traffic path A→B is: However, when link EF fails, traffic E→G shares a link CD with critical customer traffic A→B. In this case, d1+d2≤10 is required to ensure that the critical link (i.e., the link traversed by the critical customer traffic) is not overloaded, which is the area shown by the red triangle in the diagram. The blue area in the diagram represents the feasible area. At this time, the probability of overload on the path of critical customer traffic A→B decreases to The goal of this paper is to probabilistically verify the availability of critical traffic links under different failure scenarios, taking into account the total network traffic.

[0038] In practical applications, the network link availability assessment method of this disclosure can be deployed on various terminal devices through software or hardware modifications. The terminal devices involved in this disclosure can refer to devices with wireless and / or wired connection functions. Wireless connection means that they can connect to other devices via wireless methods such as Wi-Fi and Bluetooth. The terminal devices involved in this disclosure can also communicate with other devices via wired connection functions. The terminal devices involved in this disclosure can be touchscreen, non-touchscreen, or screenless. Touchscreen devices can be controlled by clicking or swiping on the display screen using fingers or styluses. Non-touchscreen devices can connect to input devices such as mice, keyboards, and touch panels to control the terminal device. Screenless devices can be, for example, screenless Bluetooth speakers. For example, the terminal devices in this application can include, but are not limited to, user equipment (UE), mobile devices, mobile terminals, handheld devices, tablet computers, laptops, PDAs, and computing devices.

[0039] The network link availability assessment method of this disclosure can also be deployed on a server. This server can be located in the cloud or locally, and can be a physical device or a virtual device, such as a virtual machine or container, with wireless communication capabilities. These wireless communication capabilities can be configured in the server's chip (system) or other components. It can refer to a device with wireless connectivity, meaning it can connect to other servers or terminal devices via wireless connections such as Wi-Fi or Bluetooth. The server involved in this disclosure can also have wired communication capabilities. For example, the server in this disclosure can be located in the cloud, communicating with terminal devices, receiving network configuration information, traffic demand information, and fault scenario information sent by the terminal devices, and using the assessment method deployed on the server based on the aforementioned network configuration information, traffic demand information, and fault scenario information to obtain the link availability assessment result corresponding to the target network, and returning it to the terminal device so that the generated link availability assessment result can be displayed to the user on the terminal device.

[0040] Figure 2 A flowchart illustrating a link availability assessment method in a network according to an embodiment of this disclosure is shown. Figure 2 As shown, the method includes steps S11 to S13.

[0041] In step S11, network configuration information, traffic demand information, and fault scenario information of the target network to be evaluated are obtained. The network configuration information includes the capacity and weight of each link in the target network. The traffic demand information includes the expected traffic set in the target network and the traffic size corresponding to each traffic in the traffic set. The fault scenario information includes the expected fault scenario set in the target network and the link that fails in each fault scenario in the fault scenario set.

[0042] In practical applications, the network configuration information mentioned above can be obtained by acquiring the network topology of the target network to be evaluated. Furthermore, traffic demand information can be constructed based on historical experience or historical traffic data. For example, let D represent the traffic between every two network nodes in the target network at a certain moment (i.e., a set of traffic). For each traffic d in D, an upper limit value u can be defined. d and lower limit value l d , and a in [u d ,l d A continuous random variable x that is uniformly distributed within an interval d x d Let d represent the size of the traffic. H is a subset of D, representing the subset of critical traffic. X is the set of all traffic sizes, where x represents the size of each traffic size in X. d If d∈D, then x d This represents the flow rate d.

[0043] Here, critical traffic can be traffic from key customers. Those skilled in the art can designate at least one traffic item in the traffic set D as critical traffic H according to actual needs. That is, critical traffic is at least one specified traffic item in the traffic set. For example, traffic between a specified initiating node and a specified destination node in the target network can be defined as critical traffic. Figure 1a The traffic from A to B can be designated as critical traffic, and can also be... Figure 1a Traffic from A to G can be designated as critical traffic, etc. This can be customized according to the actual traffic needs of key customers in the actual situation, and this embodiment of the disclosure does not impose any restrictions on it. It should be understood that since the amount of traffic in the network fluctuates, upper and lower limits can be set for the amount of traffic in each traffic segment to indicate the range of traffic variation. The upper and lower limits for each traffic segment can be set based on historical experience, and this embodiment of the disclosure does not impose any restrictions on it.

[0044] This can be achieved by estimating potential fault scenarios in the target network based on the links contained in the network topology, thus obtaining a set of fault scenarios. For example, for... Figure 1a The network topology shown may include network conditions where one or more links in AC, CD, BD, CE, CD, DF, EF, and FG fail (i.e., network conditions after link failure). The failure scenarios may also include scenarios where no links fail. The failure scenarios can characterize the network conditions resulting from link failures in the target network.

[0045] In step S12, based on network configuration information, traffic demand information, and fault scenario information, the probability of no overload on the critical link under each fault scenario is determined. The probability of no overload on the critical link under each fault scenario represents the probability that the link through which the critical traffic passes will not be overloaded under each fault scenario.

[0046] In one possible implementation, step S12, based on network configuration information, traffic demand information, and fault scenario information, determines the probability of no overload on the critical link under each fault scenario, which may include:

[0047] Step S121: For the f-th fault scenario in the fault scenario set, based on the network configuration information, traffic demand information and fault scenario information, determine whether the key traffic in the f-th fault scenario can reach the destination node from the corresponding initiating node in the target network. The key traffic is at least one traffic specified in the traffic set.

[0048] Step S122: If the critical traffic in the f-th fault scenario cannot reach the destination node from the corresponding initiating node in the target network, determine that the probability of no overload on the critical link in the f-th fault scenario is 0.

[0049] Step S123: If the critical traffic in the f-th failure scenario can reach the destination node from the corresponding initiating node in the target network, determine the critical link set and related traffic set corresponding to the critical traffic in the f-th failure scenario based on network configuration information, traffic demand information, and failure scenario information; and,

[0050] Step S124: Based on the set of critical links corresponding to the critical traffic in the f-th fault scenario and the set of related traffic, determine the probability that the critical links in the f-th fault scenario are not overloaded.

[0051] The critical link set includes the links traversed by the critical traffic in the f-th failure scenario, and the related traffic set includes the related traffic that shares at least one link with the critical traffic in the f-th failure scenario, as well as the traffic size of the related traffic.

[0052] In step S121, given the aforementioned network configuration information, traffic demand information, and fault scenario information, for each fault scenario, all paths traversed by the critical traffic from the originating node to the destination node in each fault scenario can be obtained. Furthermore, based on the faulty links in the f-th fault scenario, it can be determined whether all paths traversed by the critical traffic from the originating node to the destination node in the f-th fault scenario are fault-free. If there are fault-free paths between the critical traffic from the originating node and the destination node in the f-th fault scenario, it means that the critical traffic can reach the destination node from the corresponding originating node in the f-th fault scenario. Conversely, if all paths traversed by the critical traffic from the originating node to the destination node in the f-th fault scenario are faulty, it means that the critical traffic cannot reach the destination node from the corresponding originating node in the f-th fault scenario. From originating node to destination node, for example, critical traffic A→B can travel from originating node A to destination node B via two paths: "A→C→D→B" and "A→C→E→F→D→B". If the f-th failure scenario is a failure of link EF or link CD, the critical traffic can still reach destination node B from originating node A via the path "A→C→D→B" or "A→C→E→F→D→B". In this case, it means that the critical traffic in the f-th failure scenario can reach the destination node from the corresponding originating node in the target network. However, if the f-th failure scenario is any of the following: failure of link AC, failure of link DB, or failure of both links EF and CD, the critical traffic cannot reach destination node B from originating node A via any path. In this case, it means that the critical traffic in the f-th failure scenario cannot reach the destination node from the corresponding originating node in the target network.

[0053] In step S122, if the critical traffic in the f-th fault scenario cannot reach the destination node from the corresponding initiating node in the target network, it means that all paths that the critical traffic in the f-th fault scenario can take have faulty links. At this time, the critical traffic can no longer be transmitted to the destination node in the target network. In this case, the probability of no overload on the critical link in the f-th fault scenario can be directly determined as 0.

[0054] In step S123, as described above, given the network configuration information, traffic demand information, and fault scenario information, for each fault scenario, the links traversed by the critical traffic in each fault scenario can be obtained. That is, the links traversed by the critical traffic on the path from the originating node to the destination node in the f-th fault scenario can be obtained. If there are two or more paths for the critical traffic to reach the destination node from the originating node in a fault scenario, the shortest path can be selected based on the link weights, and the links traversed on the shortest path for the critical traffic to reach the destination node in the f-th fault scenario can be determined. For example, for... Figure 1a The network topology shown shows that when a link EF fails, the critical traffic A→B can travel from the originating node A to the destination node B via the path "A→C→D→B" through links AC, CD, and DB. When a link CD fails, the critical traffic A→B can travel from the originating node A to the destination node B via the path "A→C→E→F→D→B" through links AC, CE, EF, FD, and DB. This gives us the set of critical links corresponding to the critical traffic A→B in this failure scenario.

[0055] It should be understood that by using the same implementation method as for determining the set of associated links, it is possible to obtain the links traversed on the path of each other traffic in the traffic set, excluding critical traffic, from its respective originating node to its destination node. For example, for... Figure 1a In a failure scenario where link EF fails, the links traversed on the path "E→C→D→F→G" from originating node E to destination node G for another traffic flow E→G include EC, CD, DF, and FG. Furthermore, given the links traversed on the path from the originating node to the destination node for the critical traffic, and the links traversed on the paths from the originating node to the destination node for each other traffic in the traffic set, we can obtain the relevant traffic that shares at least one link with the critical traffic in the f-th failure scenario, and the size of that relevant traffic. Sharing at least one link means traversing at least one identical link. For example... Figure 1a In a failure scenario where link EF fails, if traffic E→G shares the same link CD as critical traffic A→B, then traffic E→G is related to critical traffic A→B. However, if... Figure 1a In a fault scenario where link CE fails, the links that traffic E→G passes through include EF and FG. Since traffic E→G does not share any link with critical traffic A→B, traffic E→G is not related to critical traffic A→B. However, if another traffic A→G shares two links AC and CD with critical traffic A→B, then traffic A→G is related to critical traffic A→B. Thus, we obtain the set of related traffic for critical traffic A→B in this fault scenario.

[0056] For example, given the f-th fault scenario, Let F be the set of failure scenarios, where f can represent the set of links that fail under that scenario. Then the set of critical links is Υ. f It can be defined as formula (1):

[0057]

[0058] in, It is an indicator, when When the value is 1, it means that the critical traffic h passed through link e in the f-th failure scenario. H represents the set of critical traffic, and E represents all links in the target network.

[0059] And, the set of critical traffic-related links Θ f It can be defined as formula (2):

[0060]

[0061] in, It is an indicator, when When υ equals 1, it means that any traffic d in the traffic set D of the target network passes through the critical link v (i.e., the link through which the critical traffic passes) in the f-th failure scenario, where υ is the set of critical links Υ. f For any link in Θ f That is, the set of related traffic that shares at least one link with at least one critical traffic in H in the f-th failure scenario.

[0062] In step S124, based on the set of critical links corresponding to the critical traffic in the f-th failure scenario and the set of related traffic, the probability that the critical links in the f-th failure scenario are not overloaded is determined, which may include:

[0063] Step S1241: Based on the set of critical links corresponding to the critical traffic in the f-th fault scenario and the set of related traffic, determine the overload of the set of traffic demand values ​​that the links through which the critical traffic passes in the f-th fault scenario do not experience overload.

[0064] Step S1242: By calculating the product of the differences between the upper and lower limits of each relevant traffic in the relevant traffic set, the super volume of the set of total traffic demand values ​​of the links through which the critical traffic passes under the f-th fault scenario is obtained.

[0065] Step S1243: The ratio between the overvolume of the set of traffic demand values ​​and the overvolume of the set of total traffic demand values ​​is determined as the probability that the critical link is not overloaded under the f-th fault scenario.

[0066] In step S1241, based on the set of critical links corresponding to the critical traffic in the f-th failure scenario and the set of related traffic, the excess volume of the set of traffic demand values ​​for which the links traversed by the critical traffic in the f-th failure scenario do not experience overload is determined, which may include:

[0067] Step S12411: Based on the set of critical links corresponding to the critical traffic and the set of related traffic under the f-th fault scenario, determine the Lebesgue equation for the critical link without overload under the f-th fault scenario. The Lebesgue equation for the critical link without overload is expressed as a value of 1 when the link through which the critical traffic passes does not experience overload, and a value of 0 when the link through which the critical traffic passes experiences overload.

[0068] Step S12412: By integrating the Lebesgue equation for critical links without overload over the n-dimensional real number field, the hypervolume of the set of traffic demand values ​​that the links through which the critical traffic passes under the f-th failure scenario do not experience overload is obtained, where n is the number of related traffic in the related traffic set corresponding to the critical traffic.

[0069] In step S12411, given the links traversed by the critical traffic in the f-th failure scenario, and the related traffic and its magnitude that share at least one link with the critical traffic in the f-th failure scenario, the total traffic generated on each critical link (i.e., the link traversed by the critical traffic) in the f-th failure scenario can be calculated. Then, the total traffic generated on each critical link in the f-th failure scenario can be compared with the capacity corresponding to each critical link. If the total traffic generated on a critical link is less than or equal to the capacity corresponding to that critical link, it means that the critical link is not overloaded; conversely, if the total traffic generated on a critical link is greater than the capacity corresponding to that critical link, it means that the critical link is overloaded. Thus, the Lebesgue equation for no overload on the critical links corresponding to the f-th failure scenario can be constructed. For example, let the set of related traffic Θ be... f The relevant traffic includes the critical traffic itself, given the f-th failure scenario. Give the relevant flow d i and flow rate Represents the relevant traffic d i The flow rate, |Θ f | represents the number of relevant traffic items in the relevant traffic set, C υ Let υ represent the capacity of the critical link. Then, the Lebesgue equation for the critical link without overload can be expressed as formula (3):

[0070]

[0071] Formula (3) can be understood as follows: given the traffic volume x and the fault scenario f, when the critical link is not overloaded, that is, when the link through which the critical traffic passes is not overloaded, The value is 1, otherwise, The value is 0.

[0072] Furthermore, in step S12412, based on the critical link no-overload Lebesgue equation shown in the above formula (3), the critical link no-overload Lebesgue equation is integrated over the n-dimensional real number field, which can be expressed as: in, Representative | Θ f |Θ| real number field, n=|Θ f |, where n is the set of related traffic corresponding to the key traffic Θ f The number of related traffic items, that is, the number of related traffic items that share at least one link with the critical traffic in the f-th failure scenario.

[0073] Considering, calculation This requires integration operations in a high-dimensional space, which is computationally intensive and cannot be completed within a finite time. Therefore, to improve computational efficiency, commonly used algorithms known in the field for calculating the volume of high-dimensional spaces, such as the Markov Chain Monte Carlo (MCMC) algorithm, can be used. Numerical estimation is performed, specifically, the integral value of the critical link's Lebesgue equation without overload can be estimated in the n-dimensional real domain using the Markov chain Monte Carlo algorithm. The estimated integral value is the hypervolume of the set of traffic demand values ​​for the links traversed by the critical traffic in the f-th failure scenario without overload. This disclosure does not limit the estimation process of the Markov chain Monte Carlo algorithm, as long as the integral value of the critical link's Lebesgue equation without overload in the n-dimensional real domain can be determined. It should be understood that the above-described estimation of the integral value using the Markov chain Monte Carlo algorithm is one possible implementation provided by this disclosure. In fact, inspired by this disclosure, those skilled in the art can use other known high-dimensional space numerical estimation techniques to estimate the integral value of the critical link's Lebesgue equation without overload in the n-dimensional real domain, and this disclosure does not limit this approach.

[0074] As mentioned above, the amount of traffic in a network fluctuates, therefore, for each traffic d in the traffic set D, an upper limit value u can be defined. d and lower limit value l d That is, the variation range of each traffic is predefined. Therefore, in step S1242, the relevant traffic in the relevant traffic set can include the critical traffic itself. Thus, the super-volume of the set of total traffic demand values ​​of the links traversed by the critical traffic in the f-th failure scenario can be obtained by calculating the product of the differences between the upper and lower limits of each relevant traffic in the relevant traffic set. For example, let the relevant traffic set Θ f If the relevant traffic includes the critical traffic itself, then the hypervolume of the set of total traffic demand values ​​of the links traversed by the critical traffic in the f-th failure scenario can be expressed as: Among them, u d Represents the relevant traffic set Θ f The upper limit of the relevant flow d, l d Represents u d Represents the relevant traffic set Θ f The lower limit of the relevant flow d.

[0075] Hypervolume based on the above set of traffic demand values and the supervolume of the set of total flow demand values In step S1243, the ratio between the overvolume of the set of traffic demand values ​​and the overvolume of the set of total traffic demand values ​​is determined as the probability that the critical link is not overloaded under the f-th fault scenario, which can be expressed as formula (4):

[0076]

[0077] Where, ρ f This represents the ratio of the range of traffic demand in the f-th failure scenario where the critical link does not experience overload to the total range of traffic demand changes, which is also the probability that the critical link will not experience overload in the f-th failure scenario.

[0078] As mentioned above, the Markov chain Monte Carlo algorithm can be used to... Numerical estimation is performed. Based on this, embodiments of this disclosure provide a calculation process for calculating the overload probability of a critical link based on the Markov chain Monte Carlo algorithm. The input to this calculation process is the relevant traffic set Θ. f The output, ρ, is the probability ρ that the critical link is not overloaded, along with the traffic link matrix Γ (which represents the links traversed by each traffic flow in the traffic set from its corresponding originating node to its destination node in the f-th failure scenario). f The calculation process includes:

[0079] 1. Define b as v i ∈Υ f That is, b is defined to include the capacity corresponding to each critical link in the set of critical links;

[0080] 2. Define n as |Θ f |, define N as 400×nlogn, where N represents the number of sampling points;

[0081] 3. Define a polyhedron P f For {x|xΓ≤b}, that is, polyhedron P f It consists of x that satisfy xΓ≤b, where x represents the traffic on the link;

[0082] 4. Calculate the Chebyshev sphere B(c, r) centered at c. min ) and the outer ball B(c,rmax ), where Chebyshev sphere B(c,r) can be defined. min It must be in polyhedron P f Inner, outer ball B(c,r) max It must contain polyhedron P. f And the centers c of the two balls are the same;

[0083] 5. Calculate α. (i.e. r) min The result of multiplying the logarithm by n and rounding down, and calculating β as... (i.e. r) max (The result of multiplying the logarithm by n and rounding up);

[0084] 6. Calculate polyhedron P f With the outside ball B(c,r) max The intersection of Q) β .

[0085] 7. Randomly generate a point p in the intersection Q β In this context, we use it as the initial element of set S, that is, set S is initialized to {p}.

[0086] 8. Initialization and initialization υ i =1;

[0087] 9. Perform the following iterations from β to α+1 to gradually reduce the search space:

[0088] 9.1. Calculate the intersection Q i-1 For P f ∩B(c,2 (i-1) / n That is, to calculate polyhedron P f With c as the center and a radius of 2 (i -1) / n The intersection of the balls Q i-1 ;

[0089] 9.2. Calculate the value of Q in set S. i-1 The number of points within the range is denoted as count_prev;

[0090] 9.3. Remove elements from set S that are not in set Q i-1 The point in the middle;

[0091] 9.4. Calculate the number of remaining points in set S, denoted as count;

[0092] 9.5. For j ranging from 1 to N, execute the following loop:

[0093] 9.5.1. Generate a random point p, where p∈Q i-1 ;

[0094] 9.5.2. If a random point p lies on a point centered at c with a radius of 2... i / n Inside the sphere, that is, if Then update count = count + 1, and add the random point p to set S;

[0095] 9.6 Update

[0096] 10. Update That is, the ρ calculated in step 9 f Dividing by the product of the differences between the upper and lower limits of each relevant traffic value in the relevant traffic set yields the final calculated probability ρ of no overload on the critical link. f .

[0097] It should be understood that for each failure scenario in the set of failure scenarios, steps S121 to S124 above can be referred to, or the calculation process described above can be specifically followed, to calculate the corresponding critical link's probability of no overload using the Markov Chain Monte Carlo algorithm. This method transforms the original #P problem into a #SAT problem, allowing for an accurate estimation of the critical link's probability of no overload within a finite time. Then, based on the critical link of the failure scenario and the probability of the failure scenario occurring, the estimated value of the critical link's availability probability of no overload is obtained, which is the link availability assessment result for the target network.

[0098] In step S13, the link availability assessment result for the target network is determined based on the probability of no overload on the critical link under each fault scenario and the probability of occurrence of each fault scenario. The link availability assessment result represents the probability that the link through which the critical traffic in the target network passes will not be overloaded under any fault scenario.

[0099] In practical applications, given a set of fault scenarios F, the sum of the occurrence probabilities of all fault scenarios in the set can be set to 1, i.e., ∑ f∈F Pr(f) = 1, where Pr(f) represents the probability of occurrence of the f-th fault scenario. The probability of occurrence of a fault scenario can be understood as the probability of a fault scenario occurring. It should be understood that those skilled in the art can set the probability of occurrence of each fault scenario in the fault scenario set F according to actual conditions and historical experience, as long as the sum of the probabilities of occurrence of all fault scenarios is 1. This disclosure does not impose any restrictions on this.

[0100] Based on this, the above-mentioned determination of the link availability assessment result of the target network according to the probability of no overload on the critical link under each failure scenario and the occurrence probability corresponding to each failure scenario can include: multiplying the probability of no overload on the critical link under each failure scenario by the occurrence probability corresponding to each failure scenario and then summing them to obtain the link availability assessment result of the target network. For example, the link availability assessment result Ω of the target network can be expressed as formula (5):

[0101]

[0102] Ω can be understood as an indicator for evaluating the availability of critical links in the entire target network, that is, it can measure the probability that the links through which critical traffic in the target network passes will not be overloaded under any failure scenario.

[0103] In practical applications, after obtaining the link availability assessment results of the target network, ISPs and network administrators can optimize the network configuration of the target network based on the link availability assessment results. For example, they can add network devices to increase links, increase link capacity, etc., to ensure the high availability of links through which critical customer traffic passes and improve service levels. This disclosure does not limit this aspect.

[0104] According to the evaluation method of this disclosure, by obtaining the network configuration information, traffic demand information, and fault scenario information of the target network to be evaluated, the probability that the links traversed by critical traffic in each fault scenario will not be overloaded can be determined. Combined with the probability of occurrence of each fault scenario, the availability evaluation result of the link non-overload attribute of the target network under various fault scenarios can be effectively determined. That is, considering the link fault scenarios, the availability of any critical traffic traversed by the link in the entire target network can be effectively quantitatively evaluated. In other words, it can scientifically and effectively evaluate whether the links on the critical traffic path will not be overloaded under various fault scenarios, thereby providing stronger support for ISPs and network administrators to ensure the high availability of network services.

[0105] Considering that in reality, critical traffic and related traffic may share the same links or pass through the same network nodes under different failure scenarios, the probability of no overload on the critical links corresponding to different failure scenarios is actually the same. For example, for Figure 1aThe network topology shown assumes two traffic demands: critical traffic A→B and related traffic A→G (the path of related traffic A→G is "A→C→D→F→G"). In both the link CE failure scenario and the link EF failure scenario, critical traffic A→B and related traffic A→G share links AC and CD, meaning they both pass through nodes A, C, and D. The critical link no-overload probability calculated using the above method is actually the same for both failure scenarios. Therefore, to reduce computational load and improve efficiency, these two failure scenarios can be combined. Alternatively, the critical link no-overload probability of one failure scenario can be calculated and used directly as the critical link no-overload probability of the other failure scenario.

[0106] Therefore, in one possible implementation, step S12 above, which determines the probability of no overload on the critical link under each fault scenario based on network configuration information, traffic demand information, and fault scenario information, may include:

[0107] Step S1201: For the f-th fault scenario in the fault scenario set, based on the network configuration information, traffic demand information and fault scenario information, determine whether the key traffic in the f-th fault scenario can reach the destination node from the corresponding initiating node in the target network. The key traffic is at least one traffic specified in the traffic set.

[0108] Step S1202: If the critical traffic in the f-th fault scenario cannot reach the destination node from the corresponding initiating node in the target network, determine that the probability of no overload on the critical link in the f-th fault scenario is 0.

[0109] Step S1203: If the critical traffic in the f-th fault scenario can reach the destination node from the corresponding initiating node in the target network, determine the critical endpoint matrix corresponding to the f-th fault scenario based on the network configuration information, traffic demand information and fault scenario information. The critical endpoint matrix represents the node information of the first and last nodes of each traffic in the traffic set that overlap with the network nodes of the critical traffic in the f-th fault scenario.

[0110] Step S1204: If the critical endpoint matrix corresponding to the f-th fault scenario is equivalent to the critical endpoint matrix corresponding to the m-th fault scenario in the fault scenario set where the critical link has no overload probability, the critical link no overload probability corresponding to the f-th fault scenario is determined as the critical link no overload probability corresponding to the m-th fault scenario.

[0111] Step S1205: If the critical endpoint matrix corresponding to the f-th fault scenario is not equivalent to the critical endpoint matrix corresponding to the fault scenarios in the fault scenario set where the critical links have been determined to have no overload probability, then based on the network configuration information, traffic demand information, and fault scenario information, determine the set of critical links and the set of related traffic corresponding to the critical traffic in the f-th fault scenario; and,

[0112] Step S1206: Based on the set of critical links corresponding to the critical traffic in the f-th fault scenario and the set of related traffic, determine the probability that the critical links in the f-th fault scenario are not overloaded.

[0113] Steps S1201 to S1202 can be implemented with reference to the specific implementation of steps S121 to S122 in the above-described embodiments of this disclosure, and are not limited herein.

[0114] In step S1203, as described above, based on network configuration information, traffic demand information, and fault scenario information, it is known whether each traffic in the traffic set shares at least one link with the critical traffic in the f-th fault scenario, and if a shared link exists, the at least one shared link. Therefore, it is also possible to know the information of the first and last nodes that overlap with the nodes traversed by the critical traffic h in the f-th fault scenario. For example, for... Figure 1a The network topology shown illustrates a failure scenario where link EF fails. The network nodes along the path of the relevant traffic A→G (A→C→D→F→G) overlap with those along the path of the critical traffic A→B. The first node is A, and the last node is D. In other words, the first and last nodes of the two overlapping traffic flows are A and D. Similarly, the first and last nodes of the relevant traffic E→G (E→C→D→F→G) overlap with the critical traffic A→B are C and D. Therefore, the critical endpoint matrix in this failure scenario can record the node information of the first and last nodes A and D that overlap between the relevant traffic A→G and the critical traffic A→B, as well as the node information of the first and last nodes C and D that overlap between the relevant traffic E→G and the critical traffic A→B.

[0115] The node information can be used to uniquely identify network nodes, for example, by using a unique number of the network node or an IP address, etc., and this embodiment of the present disclosure does not limit this. Of course, the critical endnode matrix can also use different values ​​to indicate the first and last nodes that overlap between traffic. For example, the critical endnode matrix can be defined as a |V|×|V| matrix ST, where |V| represents the total number of nodes in the target network, and each element in the matrix ST can be defined as formula (6):

[0116]

[0117] Where, d i,j Represents node v i to node v j Traffic. Defined as formula (7):

[0118]

[0119] in, This represents the traffic d in the f-th fault scenario. i,j The set of all links traversed, Let f represent the set of all links through which the critical traffic h passes under the f-th failure scenario, i.e., the critical link set; Equation (7) represents when When the intersection between them is not empty, The value is (i-1)×|V|+j, otherwise, The value is -1.

[0120] It should be understood that for each failure scenario, a corresponding critical node matrix ST can be calculated. Each element ST[i][j] in the matrix ST can represent the hash value of a pair of nodes, which is derived from node v. i to node v j The first and last nodes in the intersection of the nodes traversed by the traffic and the nodes traversed by the critical traffic can be represented by the hash values ​​of the first and last nodes of the two overlapping traffic flows (i.e., the first and last overlapping nodes).

[0121] Specifically, if the critical endpoint matrix ST of the f-th fault scenario is equivalent (i.e., identical) to the critical endpoint matrix of another m-th fault scenario in the fault scenario set, then the two fault scenarios are considered equivalent. In this case, the probability of no overload on the critical link of the f-th fault scenario is the same as the probability of no overload on the critical link of the other m-th fault scenario, i.e., ρ f =ρ mThe m-th fault scenario can be any fault scenario in the fault scenario set for which the critical link has no overload probability has been calculated, m∈F. Therefore, the random approximation stage of the f-th fault scenario (i.e., the stage of estimating the critical link no overload probability using the Markov chain Monte Carlo algorithm) can be skipped, and the critical link no overload probability corresponding to the f-th fault scenario can be determined as the critical link no overload probability corresponding to the m-th fault scenario. Since the critical endpoint matrix ST of the f-th fault scenario is not equivalent to (i.e., not the same as) the critical endpoint matrices of each fault scenario in the fault scenario set for which the critical link no overload probability has been determined, the estimation process of the Markov chain Monte Carlo algorithm for the f-th fault scenario can be executed, and the value of the critical node matrix of the f-th fault scenario and the calculated critical link no overload probability h can be recorded. f That is, to perform the above steps S1205 to S1206, wherein steps S1205 to S1206 can refer to the specific implementation of steps S123 to S124 in the above embodiments of this disclosure. In this way, the calculation speed of the entire evaluation process can be significantly improved.

[0122] Based on the evaluation method implemented by combining the above-mentioned fault scenario merging method with the Markov chain Monte Carlo algorithm, this disclosure also provides... Figure 3 The diagram illustrates a workflow for link availability assessment, as shown below. Figure 3 As shown, the workflow includes: based on a specific fault scenario in the network topology (i.e., a link DF fault, the critical traffic is A→C, the critical links are the links through which the critical traffic A→C passes, and the critical nodes are the network nodes through which the critical traffic A→C passes), inputting network configuration information and traffic demand information, generating a critical endpoint matrix for this fault scenario (the matrix shown in the figure, where elements can represent the hash values ​​of the first and last nodes that overlap with the critical traffic), merging fault scenarios (i.e., executing steps S1201 to S1204 above), and executing the Markov chain Monte Carlo algorithm (i.e., executing steps S1205 to S120 above). 6) Obtain the probability that the critical link is not overloaded in the current failure scenario, and continue to calculate the probability that the critical link is not overloaded in the next failure scenario until the probability that the critical link is not overloaded in all failure scenarios is obtained. Then, combine the occurrence probability of each failure scenario to obtain the link availability assessment result corresponding to the network topology. That is, by inputting network configuration information, traffic demand information and failure scenario information, the critical endpoint matrix of the failure scenario can be obtained. Based on this, failure scenarios are merged, and then Markov chain Monte Carlo calculation is performed to obtain the result. The above process is repeated for the next failure scenario to obtain the probability that the critical link is not overloaded in all failure scenarios.

[0123] Based on the aforementioned fault scenario merging method combined with the Markov chain Monte Carlo algorithm for evaluation, this disclosure also provides a link availability evaluation process in a network. The input to this evaluation process is network configuration information (the network topology of the target network, i.e., the node set V = {v1, v2, ..., v...}). |V|}, link set E) and capacity and weight of each link in the network topology), traffic demand information (including critical traffic set H = {h1, h2, ..., h |H| The evaluation process includes: (the network's traffic volume), fault scenario information (including a set of fault scenarios F, and the probability of occurrence of each fault scenario Pr(f), f∈F), and outputting the network link availability assessment result Ω; the assessment process includes:

[0124] 1. Define Ω as an empty set, and define P as a set of arrays.

[0125] 2. For the f-th fault scenario in the fault scenario set F, perform the following operations:

[0126] 2.1. Initialize Q as an empty array and set the flag to 0;

[0127] 2.2. Determine the weighted adjacency matrix G for the f-th fault scenario, that is, determine the links and nodes on the path traversed by each traffic in the f-th fault scenario;

[0128] 2.3. For each critical flow h in H i Perform the following operations:

[0129] 2.3.1. If h i If the target node is unreachable in the weighted adjacency matrix G under the f-th failure scenario (i.e., critical traffic cannot reach the destination node from the initiating node), set flag to 1 and break out of the loop.

[0130] 2.3.2. Determine h i The set of nodes traversed yields the critical link set L. h ;

[0131] 2.3.3. Calculate the set K as {j|v i ∈L h};

[0132] 2.3.4. Based on G, h i Calculate the critical endpoint matrix ST with K and add it to Q;

[0133] 2.4. If flag is 1, it means that at least one critical traffic link is unreachable, therefore the critical link has no overload probability ρ in this failure scenario. f If the value is 0, proceed to the next fault scenario.

[0134] 2.4. If Q is already in P (i.e., Q∈P), update Ω to Ω+ζ. Q •Pr(f), which means merging the fault scenarios and continuing to the next fault scenario;

[0135] 2.5. Otherwise, determine the relevant traffic set Θ f and traffic link matrix Γ;

[0136] 2.6. Using the above Multiphase_MCMC (i.e., the calculation process for the probability of no overload on the critical link) based on Θ f And Γ, calculate the probability that the critical link is not overloaded in the f-th failure scenario.

[0137] ρ f ;

[0138] 2.7. Update Ω to Ω+ρ f ·Pr(f), which is about to ρ f Multiply by the probability of the f-th failure scenario Pr(f) and accumulate it to Ω;

[0139] 2.8. [The following appears to be a separate, unrelated sentence: "Judge ζ"] Q Assigned the value ρ f And add Q to P.

[0140] The above evaluation process is used to calculate the probability ρ of no overload on the critical link under each failure scenario. f This probability is calculated by considering the network topology and critical traffic demands under failure scenarios. It is estimated using a multiphase MCMC method. These probabilities are then multiplied by the corresponding failure scenario occurrence probability Pr(f) and accumulated in Ω to obtain the overall network availability assessment. This process involves reachability analysis of critical traffic demands within the network and simulation of network performance under different failure scenarios, allowing for the calculation of link availability considering critical customer traffic demands and failure scenarios.

[0141] The above evaluation process assesses network availability by simulating different failure scenarios. It determines network performance by examining the reachability of critical customer traffic demands under these scenarios. If all critical customer traffic demands are reachable, the Multiphase_MCMC method is used to estimate the probability that critical links are not overloaded under that failure scenario. This probability is then weighted according to the probability of the failure scenario and accumulated in the overall availability assessment result Ω. This evaluation process is repeated until all failure scenarios have been evaluated. Ultimately, Ω provides an approximation of the network's availability after considering all failure scenarios.

[0142] The network link availability assessment method proposed in this disclosure provides a method for evaluating the load attributes of critical path links on a network using Markov chain Monte Carlo techniques to ensure network availability. This method can be integrated into a highly efficient and accurate availability probability analysis tool, designed to verify the non-overload attributes of critical traffic in Internet Service Providers (ISPs). It also essentially constructs a refined availability probability assessment model and significantly reduces the computational burden by employing stochastic approximation methods and innovative inference algorithms. This tool is crucial for network management and can be deployed in the network operations centers of various ISPs.

[0143] Figure 4 This diagram illustrates a block diagram of a link availability assessment apparatus in a network according to an embodiment of the present disclosure, such as... Figure 4 As shown, the device includes:

[0144] The acquisition module 401 is used to acquire network configuration information, traffic demand information and fault scenario information of the target network to be evaluated. The network configuration information includes the capacity and weight of each link in the target network. The traffic demand information includes the expected traffic set in the target network and the traffic size corresponding to each traffic in the traffic set. The fault scenario information includes the expected fault scenario set in the target network and the link that fails in each fault scenario in the fault scenario set.

[0145] The determining module 402 is used to determine the probability that the critical link is not overloaded under each fault scenario based on the network configuration information, the traffic demand information and the fault scenario information. The probability that the critical link is not overloaded under each fault scenario represents the probability that the link through which the critical traffic passes under each fault scenario will not be overloaded. The critical traffic is at least one traffic specified in the traffic set.

[0146] The evaluation module 403 is used to determine the link availability evaluation result corresponding to the target network based on the probability of no overload on the critical link under each failure scenario and the probability of occurrence of each failure scenario. The link availability evaluation result represents the probability that the link through which the critical traffic in the target network passes will not be overloaded under any failure scenario.

[0147] In one possible implementation, determining the probability of no overload on the critical link under each fault scenario based on the network configuration information, the traffic demand information, and the fault scenario information includes: for the f-th fault scenario in the fault scenario set, determining whether the critical traffic under the f-th fault scenario can reach the destination node from the corresponding initiating node in the target network based on the network configuration information, the traffic demand information, and the fault scenario information; if the critical traffic under the f-th fault scenario cannot reach the destination node from the corresponding initiating node in the target network, determining that the probability of no overload on the critical link under the f-th fault scenario is 0; and determining that the probability of no overload on the critical link under the f-th fault scenario is 0. If key traffic can reach the destination node from the corresponding initiating node in the target network, based on the network configuration information, the traffic demand information, and the fault scenario information, determine the key link set and related traffic set corresponding to the key traffic in the f-th fault scenario; and, based on the key link set and related traffic set corresponding to the key traffic in the f-th fault scenario, determine the probability that the key links in the f-th fault scenario are not overloaded; wherein, the key link set includes the links traversed by the key traffic in the f-th fault scenario, and the related traffic set includes the related traffic that shares at least one link with the key traffic in the f-th fault scenario and the traffic size of the related traffic.

[0148] In one possible implementation, determining the probability of no overload on the critical link under each fault scenario based on the network configuration information, the traffic demand information, and the fault scenario information further includes: if the critical traffic under the f-th fault scenario can reach the destination node from the corresponding initiating node in the target network, determining the critical endpoint matrix corresponding to the f-th fault scenario based on the network configuration information, the traffic demand information, and the fault scenario information, wherein the critical endpoint matrix represents the node information of the first and last nodes in the network nodes traversed by each traffic in the traffic set under the f-th fault scenario that overlap with the network nodes traversed by the critical traffic under the f-th fault scenario; the critical endpoint matrix corresponding to the f-th fault scenario and the critical endpoint matrix in the fault scenario set have already determined the critical endpoint matrix. If the critical endpoint matrix corresponding to the m-th failure scenario with a determined critical link no overload probability is equivalent, then the critical link no overload probability corresponding to the f-th failure scenario is determined as the critical link no overload probability corresponding to the m-th failure scenario. If the critical endpoint matrix corresponding to the f-th failure scenario is not equivalent to the critical endpoint matrix corresponding to the failure scenarios in the failure scenario set with determined critical link no overload probabilities, then based on the network configuration information, the traffic demand information, and the failure scenario information, the critical link set and related traffic set corresponding to the critical traffic in the f-th failure scenario are determined. Finally, based on the critical link set and related traffic set corresponding to the critical traffic in the f-th failure scenario, the critical link no overload probability in the f-th failure scenario is determined.

[0149] In one possible implementation, determining the probability that the critical links in the f-th failure scenario are not overloaded, based on the set of critical links corresponding to the critical traffic and the set of related traffic in the f-th failure scenario, includes: determining the overload volume of the set of traffic demand values ​​for which the links traversed by the critical traffic in the f-th failure scenario will not experience overload, based on the set of critical links corresponding to the critical traffic and the set of related traffic in the f-th failure scenario; obtaining the overload volume of the set of total traffic demand values ​​for the links traversed by the critical traffic in the f-th failure scenario by calculating the product of the differences between the upper and lower limits of each related traffic in the set of related traffic; and determining the ratio between the overload volume of the set of traffic demand values ​​and the overload volume of the set of total traffic demand values ​​as the probability that the critical links are not overloaded in the f-th failure scenario.

[0150] In one possible implementation, determining the hypervolume of the set of traffic demand values ​​for which the links traversed by the critical traffic in the f-th failure scenario do not experience overload, based on the set of critical links and the set of related traffic corresponding to the critical traffic in the f-th failure scenario, includes: determining the Lebesgue equation for the critical links without overload corresponding to the f-th failure scenario, based on the set of critical links and the set of related traffic corresponding to the critical traffic in the f-th failure scenario, wherein the Lebesgue equation for the critical links without overload is expressed as a value of 1 when the links traversed by the critical traffic do not experience overload, and a value of 0 when the links traversed by the critical traffic experience overload; and obtaining the hypervolume of the set of traffic demand values ​​for which the links traversed by the critical traffic in the f-th failure scenario do not experience overload by integrating the Lebesgue equation for the critical links without overload over the n-dimensional real number field, where n is the number of related traffic in the set of related traffic corresponding to the critical traffic.

[0151] In one possible implementation, the apparatus further includes: an estimation module for estimating the integral value of the overload-free Lebesgue equation of the critical link in the n-dimensional real field using a Markov chain Monte Carlo algorithm, to obtain the hypervolume of the set of traffic demand values.

[0152] In one possible implementation, the sum of the occurrence probabilities of all fault scenarios in the fault scenario set is 1. The step of determining the link availability assessment result of the target network based on the probability of no overload on the critical link under each fault scenario and the occurrence probability of each fault scenario includes: multiplying the probability of no overload on the critical link under each fault scenario by the occurrence probability of each fault scenario and then summing the results to obtain the link availability assessment result of the target network.

[0153] According to the apparatus of this disclosure, by acquiring network configuration information, traffic demand information, and fault scenario information of the target network to be evaluated, the probability that the links traversed by critical traffic in each fault scenario will not be overloaded can be determined. Combined with the probability of occurrence of each fault scenario, the availability assessment result of the link non-overload attribute of the target network under various fault scenarios can be effectively determined. That is, considering the link fault scenarios, the availability of any critical traffic traversed by the link in the entire target network can be effectively quantitatively assessed, or in other words, the probability of whether the links on the critical traffic path will not be overloaded under various fault scenarios can be scientifically and effectively assessed, thereby providing stronger support for ISPs and network administrators to ensure the high availability of network services.

[0154] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0155] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method. The computer-readable storage medium can be volatile or non-volatile.

[0156] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0157] This disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0158] Figure 5 A block diagram of an electronic device 1900 according to an embodiment of the present disclosure is shown. For example, the electronic device 1900 may be provided as a server or a terminal device. (Refer to...) Figure 5 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0159] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). Electronic device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TM Mac OS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0160] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0161] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0162] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0163] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0164] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0165] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0166] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0167] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0168] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0169] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for evaluating link availability in a network, characterized in that, include: Obtain network configuration information, traffic demand information, and fault scenario information of the target network to be evaluated. The network configuration information includes the capacity and weight of each link in the target network. The traffic demand information includes the expected traffic set in the target network and the traffic size corresponding to each traffic in the traffic set. The fault scenario information includes the expected fault scenario set in the target network and the link that fails in each fault scenario in the fault scenario set. Based on the network configuration information, the traffic demand information, and the fault scenario information, the probability of no overload on the critical link under each fault scenario is determined. The probability of no overload on the critical link under each fault scenario represents the probability that the link through which the critical traffic passes will not be overloaded under each fault scenario. The critical traffic is at least one traffic specified in the traffic set. Based on the probability of no overload on critical links under each failure scenario and the probability of occurrence of each failure scenario, the link availability assessment result corresponding to the target network is determined. The link availability assessment result represents the probability that the links through which critical traffic in the target network passes will not be overloaded under any failure scenario. The step of determining the probability of no overload on the critical link under each fault scenario based on the network configuration information, the traffic demand information, and the fault scenario information includes: For the f-th fault scenario in the set of fault scenarios, based on the network configuration information, the traffic demand information and the fault scenario information, it is determined whether the critical traffic in the f-th fault scenario can reach the destination node from the corresponding initiating node in the target network; If the critical traffic in the f-th failure scenario cannot reach the destination node from the corresponding initiating node in the target network, the probability that the critical link in the f-th failure scenario is not overloaded is determined to be 0. If, under the f-th failure scenario, the critical traffic can reach the destination node from the corresponding initiating node in the target network, then, based on the network configuration information, the traffic demand information, and the failure scenario information, the critical link set and related traffic set corresponding to the critical traffic under the f-th failure scenario are determined; and, Based on the set of critical links corresponding to the critical traffic in the f-th fault scenario and the set of related traffic, determine the probability that the critical links in the f-th fault scenario are not overloaded; The critical link set includes the links through which critical traffic passes in the f-th failure scenario, and the related traffic set includes related traffic that shares at least one link with the critical traffic in the f-th failure scenario, as well as the traffic size of the related traffic. The step of determining the probability that the critical links in the f-th fault scenario are not overloaded, based on the set of critical links corresponding to the critical traffic and the set of related traffic in the f-th fault scenario, includes: Based on the set of critical links corresponding to the critical traffic in the f-th fault scenario and the set of related traffic, determine the over-volume of the set of traffic demand values ​​for which the links through which the critical traffic in the f-th fault scenario do not experience overload. The hypervolume of the set of total traffic demand values ​​of the links through which the critical traffic passes under the f-th fault scenario is obtained by calculating the product of the differences between the upper and lower limits of each relevant traffic in the relevant traffic set. The ratio between the overvolume of the set of traffic demand values ​​and the overvolume of the set of total traffic demand values ​​is determined as the probability that the critical link is not overloaded in the f-th fault scenario.

2. The method according to claim 1, characterized in that, The step of determining the probability of no overload on the critical link under each fault scenario based on the network configuration information, the traffic demand information, and the fault scenario information further includes: If the critical traffic in the f-th fault scenario can reach the destination node from the corresponding initiating node in the target network, the critical endpoint matrix corresponding to the f-th fault scenario is determined based on the network configuration information, the traffic demand information, and the fault scenario information. The critical endpoint matrix represents the node information of the first and last nodes of each traffic in the traffic set that overlap with the network nodes traversed by the critical traffic in the f-th fault scenario. If the critical endpoint matrix corresponding to the f-th fault scenario is equivalent to the critical endpoint matrix corresponding to the m-th fault scenario in the fault scenario set where the critical link has been determined to have no overload probability, then the critical link no overload probability corresponding to the f-th fault scenario is determined as the critical link no overload probability corresponding to the m-th fault scenario. If the critical endpoint matrix corresponding to the f-th fault scenario is not equivalent to the critical endpoint matrix corresponding to the fault scenario in the fault scenario set for which the critical link has been determined to have no overload probability, then based on the network configuration information, the traffic demand information, and the fault scenario information, determine the critical link set and related traffic set corresponding to the critical traffic in the f-th fault scenario; and based on the critical link set and related traffic set corresponding to the critical traffic in the f-th fault scenario, determine the critical link no overload probability in the f-th fault scenario.

3. The method according to claim 1, characterized in that, The process of determining the overload of the set of traffic demand values ​​for which the links traversed by the critical traffic in the f-th fault scenario do not experience overload, based on the set of critical links corresponding to the critical traffic in the f-th fault scenario and the set of related traffic, includes: Based on the set of critical links and related traffic sets corresponding to the critical traffic in the f-th fault scenario, the Lebesgue equation for the critical link without overload corresponding to the f-th fault scenario is determined. The Lebesgue equation for the critical link without overload is expressed as a value of 1 when the link through which the critical traffic passes does not experience overload, and a value of 0 when the link through which the critical traffic passes experiences overload. By integrating the Lebesgue equation for the critical link without overload over the n-dimensional real number field, the hypervolume of the set of traffic demand values ​​for the links through which the critical traffic passes under the f-th fault scenario does not experience overload is obtained, where n is the number of related traffic in the related traffic set corresponding to the critical traffic.

4. The method according to claim 3, characterized in that, The method further includes: The integral value of the Lebesgue equation for the critical link without overload in the n-dimensional real field is estimated using the Markov chain Monte Carlo algorithm, thus obtaining the hypervolume of the set of traffic demand values.

5. The method according to claim 1 or 2, characterized in that, The sum of the occurrence probabilities of all failure scenarios in the failure scenario set is 1. The determination of the link availability assessment result for the target network based on the probability of no overload on the critical link under each failure scenario and the occurrence probability of each failure scenario includes: The link availability assessment result for the target network is obtained by multiplying the probability of no overload on the critical link under each failure scenario by the probability of occurrence of each failure scenario and then summing the results.

6. A network link availability assessment device, characterized in that, include: The acquisition module is used to acquire network configuration information, traffic demand information, and fault scenario information of the target network to be evaluated. The network configuration information includes the capacity and weight of each link in the target network. The traffic demand information includes the expected traffic set in the target network and the traffic size corresponding to each traffic in the traffic set. The fault scenario information includes the expected fault scenario set in the target network and the link that fails in each fault scenario in the fault scenario set. The determination module is used to determine the probability that the critical link is not overloaded under each fault scenario based on the network configuration information, the traffic demand information and the fault scenario information. The probability that the critical link is not overloaded under each fault scenario represents the probability that the link through which the critical traffic passes under each fault scenario will not be overloaded. The critical traffic is at least one traffic specified in the traffic set. The evaluation module is used to determine the link availability evaluation result corresponding to the target network based on the probability of no overload of critical links under each failure scenario and the probability of occurrence of each failure scenario. The link availability evaluation result represents the probability that the links through which critical traffic in the target network passes will not be overloaded under any failure scenario. The step of determining the probability of no overload on the critical link under each fault scenario based on the network configuration information, the traffic demand information, and the fault scenario information includes: For the f-th fault scenario in the set of fault scenarios, based on the network configuration information, the traffic demand information and the fault scenario information, it is determined whether the critical traffic in the f-th fault scenario can reach the destination node from the corresponding initiating node in the target network; If the critical traffic in the f-th failure scenario cannot reach the destination node from the corresponding initiating node in the target network, the probability that the critical link in the f-th failure scenario is not overloaded is determined to be 0. If, under the f-th failure scenario, the critical traffic can reach the destination node from the corresponding initiating node in the target network, then, based on the network configuration information, the traffic demand information, and the failure scenario information, the critical link set and related traffic set corresponding to the critical traffic under the f-th failure scenario are determined; and, Based on the set of critical links corresponding to the critical traffic in the f-th fault scenario and the set of related traffic, determine the probability that the critical links in the f-th fault scenario are not overloaded; The critical link set includes the links through which critical traffic passes in the f-th failure scenario, and the related traffic set includes related traffic that shares at least one link with the critical traffic in the f-th failure scenario, as well as the traffic size of the related traffic. The step of determining the probability that the critical links in the f-th fault scenario are not overloaded, based on the set of critical links corresponding to the critical traffic and the set of related traffic in the f-th fault scenario, includes: Based on the set of critical links corresponding to the critical traffic in the f-th fault scenario and the set of related traffic, determine the over-volume of the set of traffic demand values ​​for which the links through which the critical traffic in the f-th fault scenario do not experience overload. The hypervolume of the set of total traffic demand values ​​of the links through which the critical traffic passes under the f-th fault scenario is obtained by calculating the product of the differences between the upper and lower limits of each relevant traffic in the relevant traffic set. The ratio between the overvolume of the set of traffic demand values ​​and the overvolume of the set of total traffic demand values ​​is determined as the probability that the critical link is not overloaded in the f-th fault scenario.

7. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1 to 5 when executing instructions stored in the memory.

8. A non-volatile computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 5.

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