Method, device and equipment for evaluating survivability of SPN (Shortest Path Network), and readable medium

Abstract

The invention discloses an survivability evaluation method, device and equipment of an SPN (Shortest Path Network) and a readable medium. Constructing an SPN network initialization model based on the actual SPN network operation state of the to-be-evaluated area, wherein the SPN network initialization model comprises network nodes corresponding to actually deployed transmission network elements and a network structure of IP routing connection relationships of the network nodes; setting node load and node capacity of each network node; setting at least one simulation attack strategy aiming at the SPN network initialization model; attacking the network nodes in the SPN network initialization model based on a simulation attack strategy, distributing the node load of the network node which fails after attack to other network nodes, and monitoring whether the redistributed network node is overloaded and fails until no new network node fails; and determining a survivability index based on the number of non-invalid nodes in the SPN network initialization model and the number of total network nodes, and performing survivability evaluation. According to the scheme of the invention, survivability evaluation for SPN network characteristics can be realized.

Description

Technical Field

[0001] This invention relates to the field of network assessment technology, and in particular to a method, apparatus, device, and readable medium for assessing the resilience of SPN networks. Background Technology

[0002] Slicing Packet Network (SPN) was proposed as a new generation of 5G transport network technology. Through a three-layer architecture of Slice Transport Layer (STL), Slice Channel Layer (SCL), and Slice Packet Layer (SPL), it achieves physical-level hard slice isolation, deterministic latency of TDM and packet fusion, and SDN-based intelligent management and control, thus becoming a core infrastructure supporting 5G commercialization.

[0003] Under the characteristics of complex networks, the core risk of SPN networks is cascading failure. That is, if a single core node fails due to an attack or equipment failure, the service load it carries may be overloaded and crashed if it is not properly distributed, which may lead to a chain reaction and eventually paralyze the entire network.

[0004] Therefore, there is an urgent need for a robustness assessment method for SPN networks, which quantifies the impact of key parameters on network robustness and provides a scientific basis for network planning and operation optimization. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and readable medium for assessing the resilience of SPN networks, thereby enabling resilience assessment of SPN network characteristics.

[0006] According to one aspect of the present invention, a method for assessing the resilience of an SPN network is provided, the method comprising:

[0007] An SPN network initialization model is constructed based on the actual SPN network operation status of the area to be evaluated. The SPN network initialization model includes network nodes that represent the actual deployed transmission network elements in the SPN network and network structures that represent the IP routing connection relationships of each network node.

[0008] Configure the node load and node capacity of each of the network nodes;

[0009] Set at least one simulated attack strategy for the SPN network initialization model;

[0010] The network nodes in the SPN network initialization model are attacked based on the simulated attack strategy. The node load of the network nodes that fail after the attack is distributed to other network nodes, and the network nodes after the redistribution are monitored for overload failure until no new network nodes fail.

[0011] Based on the number of non-failed nodes and the total number of network nodes in the SPN network initialization model, resilience indicators are determined and resilience assessment is performed.

[0012] Optionally, setting the node load and node capacity of each of the network nodes includes:

[0013] The service load data of each transmission network element in the actual operation of the SPN network is used as the initial load of each network node; an allowable coefficient is set to characterize the periodic fluctuation characteristics of the load, and the range of the allowable coefficient characterizes the fluctuation amplitude of the network load based on the initial load; the node load of each network node is set according to the initial load and the allowable coefficient.

[0014] The maximum nominal capacity of each of the transmission network element hardware interfaces is used as the capacity baseline value of each of the network nodes; a redundancy coefficient is set to characterize the degree of capacity backup, and the value of the redundancy coefficient is related to the backup configuration of the network node; the node capacity of each of the network nodes is set according to the capacity baseline value and the redundancy coefficient.

[0015] Optionally, the types of simulated attack strategies include: random attacks, deliberate attacks, and mixed attacks, wherein:

[0016] The random attack is used to randomly select and remove network nodes in the SPN network using a random number generation algorithm. The proportion of the number of network nodes removed in each attack to the total number of network nodes is a preset value.

[0017] The deliberate attack is used to sort network nodes in the SPN network from largest to smallest based on the key indicator values ​​of all network nodes, and to prioritize the removal of network nodes with higher rankings.

[0018] The hybrid attack is used to adjust the combination ratio of the random attack and the deliberate attack through the attack ratio parameter. The number of network nodes for random attack and the number of network nodes for deliberate attack are calculated according to the combination ratio. Network nodes are selected by random selection and index sorting respectively, and then merged and removed.

[0019] Optionally, the step of distributing the node load of the network node that failed after the attack to other network nodes includes:

[0020] Calculate the remaining capacity of each neighbor node of the failed network node;

[0021] The proportion of the remaining capacity of each neighbor node to the total remaining capacity of all neighbor nodes is determined as the load allocation ratio of that neighbor node;

[0022] The load of the failed network node is distributed to its neighboring nodes according to the load distribution ratio.

[0023] Optionally, the determination of resilience indicators based on the number of non-failed nodes and the total number of network nodes in the SPN network initialization model includes:

[0024] Perform connectivity analysis on the set of non-failed network nodes, and select the largest connected subgraph that is interconnected by effective connection edges. The number of network nodes contained in the subgraph is the number of effective nodes among the non-failed nodes that can realize business transmission.

[0025] The ratio of the number of network nodes in the largest connected subgraph to the total number of network nodes is used to calculate the resilience index.

[0026] Optionally, the survivability assessment includes:

[0027] Determine the target value range to which the survivability index belongs, with each value range corresponding to a different level of survivability, and determine the survivability of the current area to be evaluated based on the target value range.

[0028] Optionally, the method further includes:

[0029] Based on the aforementioned resilience, the SPN network is optimized for resilience, including one or more of the following methods: implementing real-time status monitoring of core network nodes and triggering alarms when indicators are abnormal; configuring capacity backups for critical network nodes; and controlling the actual load of network nodes through traffic scheduling.

[0030] According to another aspect of the present invention, a resilience assessment apparatus for an SPN network is provided, the apparatus comprising:

[0031] The model building unit is used to build an SPN network initialization model based on the actual SPN network operation status of the area to be evaluated. The SPN network initialization model includes network nodes that represent the actual deployed transmission network elements in the SPN network and network structures that represent the IP routing connection relationships of each network node.

[0032] The first setting unit is used to set the node load and node capacity of each of the network nodes;

[0033] The second setting unit is used to set at least one simulated attack strategy for the SPN network initialization model;

[0034] The simulated attack unit is used to attack the network nodes in the SPN network initialization model based on the simulated attack strategy, distribute the node load of the network nodes that fail after the attack to other network nodes, and monitor whether the redistributed network nodes are overloaded and fail until no new network nodes fail.

[0035] The evaluation unit is used to determine resilience indicators and conduct resilience assessments based on the number of non-failed nodes and the total number of network nodes in the SPN network initialization model.

[0036] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0037] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the SPN network resilience assessment method according to any embodiment of the present invention.

[0038] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the SPN network robustness assessment method according to any embodiment of the present invention.

[0039] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the survivability assessment method for SPN networks according to any embodiment of the present invention.

[0040] The technical solution of this invention accurately reflects the initial state of the network by establishing the initialization structure of the actual SPN network, simulates the actual network attack situation by setting a simulated attack strategy, simulates the failure propagation process and load redistribution process of the network after being attacked, and comprehensively evaluates the resilience of the network under different attack modes.

[0041] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart of a method for evaluating the resilience of an SPN network according to Embodiment 1 of the present invention;

[0044] Figure 2This is an SPN network topology diagram applicable to Embodiment 1 of the present invention;

[0045] Figure 3 This is a schematic diagram of a network load redistribution and cascading failure applicable to Embodiment 1 of the present invention;

[0046] Figure 4 This is a flowchart of a node load and node capacity setting method provided in Embodiment 2 of the present invention;

[0047] Figure 5 This is a flowchart of a network load redistribution method provided in Embodiment 2 of the present invention;

[0048] Figure 6 This is a schematic diagram of a load redistribution method applicable to Embodiment 3 of the present invention;

[0049] Figure 7 This is a schematic diagram illustrating the analysis of the destructive impact of controllable parameters applicable to Embodiment 4 of the present invention;

[0050] Figure 8 This is a schematic diagram of the structure of an SPN network resilience assessment device provided in Embodiment 5 of the present invention;

[0051] Figure 9 This is a schematic diagram of the structure of an electronic device that implements the SPN network resilience assessment method of this invention. Detailed Implementation

[0052] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0053] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0054] Example 1

[0055] Figure 1 This is a flowchart of a method for assessing the resilience of an SPN network according to Embodiment 1 of the present invention. This embodiment is applicable to assessing the resilience of an SPN network. The method can be executed by an SPN network resilience assessment device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0056] S110. Construct an SPN network initialization model based on the actual SPN network operation status of the area to be evaluated. The SPN network initialization model includes network nodes that represent the actual deployed transmission network elements in the SPN network and network structures that represent the IP routing connection relationships of each network node.

[0057] Figure 2 This is an SPN network topology diagram applicable to Embodiment 1 of the present invention. When constructing the SPN network model, each transmission network element in the SPN is considered a node; that is, the communication devices in the real SPN network are set as nodes in the model. Nodes are connected or interconnected based on IP routing, abstracted into a complex network structure diagram, represented as G = (P, L), where P represents all transmission network elements in the SPN network, such as... Figure 2 As shown, each point represents an SPN network element node. Core layer nodes have numerous connections with other core or aggregation layers, giving them very high degree values. The degree of an SPN node represents its connectivity with other network elements in the SPN network; the higher the degree value, the more network elements it connects with.

[0058] S120. Configure the node load and node capacity of each network node.

[0059] Node load refers to the actual amount of service data carried by a single network node, including the current actual amount of service data to be processed by the node and possible peak fluctuations. Node capacity refers to the maximum amount of service data a single network node can handle; exceeding this capacity will cause the node to overload and fail. Node capacity is set based on the hardware specifications and spare capacity of the transmission equipment corresponding to the node.

[0060] S130. Set at least one simulated attack strategy for the SPN network initialization model.

[0061] A network attack refers to the use of certain strategies to attack real-world network nodes or edges, causing cascading failures in the network and replicating the faults or attack risks that SPN networks may encounter in real-world scenarios.

[0062] In the embodiments of the present invention, the types of simulated attack strategies include: random attack, deliberate attack, and hybrid attack, where:

[0063] The random attack is used to randomly select and remove network nodes of the SPN network through a random number generation algorithm, and the proportion of the number of network nodes removed each time in the total number of network nodes is a preset value;

[0064] The deliberate attack is used to sort the network nodes according to the key metric values of all network nodes in the SPN network from large to small, and preferentially select the network nodes ranked靠前 for removal;

[0065] The hybrid attack is used to regulate the combination ratio of the random attack and the deliberate attack through an attack ratio parameter, calculate the number of network nodes for the random attack and the number of network nodes for the deliberate attack according to the combination ratio, and respectively select network nodes through random selection and index sorting and then merge and remove them.

[0066] Specifically, common network attacks include random attack, deliberate attack, and hybrid attack. Random attack strategy: Abstract the SPN network at the routing layer as a scale-free network, assuming that the nodes in the network randomly fail due to random errors or non-human reasons. The main idea of this random attack strategy is to randomly remove some nodes from the network. The deliberate attack strategy, compared with the random attack strategy, another common attack is the deliberate attack, which mainly sorts the node degrees in the network or the weighted edges of the weighted network, and then attacks the nodes with larger node degrees or larger weighted edge values first. The hybrid attack is between the random attack and the deliberate attack, and is to set an attack ratio (Attacked_nodes) between the random attack and the deliberate attack in advance to implement the attack. When Attacked_nodes = 0, that is, the random attack is implemented with a probability of 0%, and the hybrid attack strategy at this time is the deliberate attack strategy introduced above, belonging to a completely deterministic attack method, attacking the node with the largest node degree in the network; when, that is, the random attack is implemented with a probability of 100%, the attack strategy is the random attack strategy, belonging to a completely random attack method, that is, in the network nodes, randomly select a node for attack and destruction, regardless of the node degree size. When 0 < Attacked_nodes < 1, it is a hybrid attack. First, sort the importance of network nodes according to the deliberate attack, and then select Attacked_nodes% of the core nodes and implement a random attack.

[0067] S140. Attack the network nodes in the initialized model of the SPN network based on the simulated attack strategy, allocate the node loads of the network nodes that fail after the attack to other network nodes, and monitor whether the network nodes after the reallocation are overloaded and fail until no new network nodes fail.

[0068] The cascading failure load redistribution strategy adopts a full distribution strategy. If a network node is attacked or its services are overloaded, the services it carries will be evenly distributed to all neighboring nodes. However, in the SPN complex network propagation model, the load redistribution needs to follow the network redistribution strategy so as not to cause overload of neighboring nodes. Figure 3 This is a schematic diagram illustrating a network load redistribution and cascading failure applicable to Embodiment 1 of the present invention, as shown below. Figure 3 As shown, during network initialization, each network node is assigned a normal operating state value "E" (EfficientState), while attacked or failed nodes are assigned the state value "L" (LossEfficient). Selected attacked or failed nodes will transition from state E to L. This state change causes the damaged node to redistribute its original load to its neighboring nodes. When a node is in state L, it is removed from the network because it is already invalid. Its network service load will be redistributed to its neighboring nodes through a redistribution strategy, and each neighboring node will update its service load. If the updated service load exceeds the node's capacity, the network node will cascade and fail, its state will change to L, and a new round of service redistribution will begin. If the updated service load does not exceed the node's capacity, the node will be unaffected, and the cascading failure process will terminate at that node.

[0069] S150. Determine resilience indicators and conduct resilience assessment based on the number of non-failed nodes and the total number of network nodes in the SPN network initialization model.

[0070] After the SPN network initialization model undergoes simulated attacks and cascading failures, the non-failed nodes that can still operate normally are first selected from the model. The ratio of these non-failed nodes to the total number of nodes in the SPN network in the model is calculated, and the result is the resilience index. Based on this, the resilience of the SPN network is determined.

[0071] In this embodiment of the invention, the resilience index is determined based on the number of non-failed nodes in the SPN network initialization model and the total number of network nodes, including:

[0072] Perform connectivity analysis on the set of non-failed network nodes, and select the largest connected subgraph that is interconnected by effective connection edges. The number of network nodes contained in the subgraph is the number of effective nodes among the non-failed nodes that can realize business transmission.

[0073] The ratio of the number of network nodes in the largest connected subgraph to the total number of network nodes is used to calculate the resilience index.

[0074] The set of undamaged network nodes consists of nodes that can still function normally after a simulated attack and cascading failure. For example, in a regional SPN network with a total of 100 nodes, 80 nodes remain intact after an attack. However, not all of these 80 nodes can transmit data. For instance, nodes A, B, and C can transmit data if A is connected to B and B is connected to C. Nodes D and E, however, are connected to failed nodes or cannot transmit data to other nodes and cannot be considered valid nodes. Connecting the connectable lines together forms a set of nodes capable of transmitting services. The largest node in this set is the maximum connected subgraph, containing the number of valid nodes capable of transmitting services between each other.

[0075] In this embodiment of the invention, a survivability assessment is performed, including:

[0076] Determine the target value range of the damage resistance index. Each value range corresponds to a different level of damage resistance. Determine the damage resistance of the area to be evaluated based on the target value range.

[0077] Specifically, to better discuss the robustness of cascaded failure networks under attack scale, network tolerance coefficient, and redundancy coefficient, this embodiment of the invention introduces a resilience representation for the entire network attack node v. i After a cascading failure occurs in the network, the value of the largest connected subgraph in the network is used to represent the network's robustness and measure the degree of network collapse.

[0078] G = N′N;

[0079] Where G is the total number of nodes in the network, and N' is the number of nodes in the largest connected subgraph of the network after the cascading failure ends. The larger the value of G, the better the network robustness and the less severe the network collapse. The smaller the value of G, the worse the network robustness and the greater the network collapse. After the SPN network initialization model undergoes simulated attacks and cascading failures, the remaining operational nodes are first selected from the model. Then, the largest node cluster capable of inter-service transmission via valid IP routing is identified from these operational nodes, i.e., the largest connected subgraph. Isolated operational nodes are excluded because they cannot participate in service transmission. Subsequently, the ratio of the number of nodes in this largest connected subgraph to the total number of nodes in the SPN network in the model is calculated, and the result is the resilience index. Finally, resilience is assessed based on this index value. If the index > 0.8, it indicates that over 80% of the effective nodes are connected normally, and the network has strong resilience. If 0.3 < index ≤ 0.8, it indicates that 30%-80% of the effective nodes are connected, and the resilience is moderate. If the index ≤ 0.3 (especially ≤ 0.1), it indicates that less than 30% (or 10%) of the nodes are connected, and the network has weak resilience (or complete collapse). This quantitatively assesses the SPN network's survival and service maintenance capabilities under fault scenarios.

[0080] In this embodiment of the invention, the method may further include:

[0081] Based on resilience, SPN networks are optimized for resilience, including one or more of the following methods: real-time status monitoring of core network nodes and triggering alarms when indicators are abnormal; capacity backup is configured for critical network nodes; and the actual load of network nodes is controlled through traffic scheduling.

[0082] For networks with medium or low resilience, the focus is on core layer nodes. Key metrics are collected in real time through the network operations and maintenance platform, including current node load, device operating temperature, and IP routing connection status. When abnormal fluctuations occur in these metrics, such as a load increase from 60% to 85% within 10 minutes or a temperature exceeding the device threshold, multi-level alarms are immediately triggered to ensure that operations and maintenance personnel respond and investigate within 15 minutes, preventing minor faults from escalating into node failures.

[0083] Capacity backup is configured differently based on the importance of nodes in the network. Core layer nodes adopt a 1+1 backup mode, meaning the primary and backup devices run in real time and can switch over in milliseconds, with a redundancy coefficient of ≥1, ensuring that the capacity is at least twice the daily load. Aggregation layer nodes adopt a 1 backup mode, meaning a single set of backup devices is used after the primary device fails, with a redundancy coefficient of ≥0.5, and a capacity of more than 1.5 times the daily load. This approach is particularly suitable for networks with low resilience, reducing the risk of cascading failures by increasing capacity redundancy.

[0084] For networks with medium to low resilience, dynamic traffic scheduling is achieved using an SDN intelligent management and control system: when the load on a node is detected to be approaching a safe capacity threshold, such as 75%, non-urgent services carried by that node are automatically reassigned to adjacent nodes with loads below 50%; for core services, backup routes are pre-planned to ensure that load redistribution does not exceed the capacity limit of the target node. Ultimately, the actual load on all nodes is controlled within 80% of capacity, reserving sufficient redundancy to handle load balancing after a failure.

[0085] Example 2

[0086] Figure 4 This is a flowchart of a method for setting node load and node capacity according to Embodiment 2 of the present invention. Figure 4 As shown, the method includes:

[0087] S410. Use the service load data of each transmission network element in the actual operation of the SPN network as the initial load of each network node; set an allowable coefficient to characterize the periodic fluctuation characteristics of the load, and the range of the allowable coefficient characterizes the fluctuation range of the network load based on the initial load; set the node load of each network node according to the initial load and the allowable coefficient.

[0088] Node load represents the actual amount of service data a node needs to process. The configuration logic perfectly mirrors real-world network service changes. First, the actual service load record for each transmission network element in the SPN network of the area to be evaluated is found. For example, the daily recorded service volume of the "X Hospital-SPN980" device in a certain area is 69.45G, which is used as the initial load for that node, equivalent to the node's basic daily workload. The initial load of the SPN network is taken from the actual network service load of a certain telecommunications operator. The i-th network element is represented by L... i This means that, in order to get closer to the true value, a fluctuation coefficient is given, called the network's tolerance coefficient (CurrentLoad):

[0089] RL i =L i ×CurrentLoad.

[0090] S420: Use the maximum nominal capacity of each transmission network element hardware interface as the capacity baseline value for each network node; set a redundancy coefficient to characterize the degree of capacity backup, the value of which is related to the backup configuration of the network node; set the node capacity of each network node according to the capacity baseline value and the redundancy coefficient.

[0091] Each transmission network element's hardware interface has a maximum nominal capacity. For example, the SPN7900 device's interface is nominally capable of transmitting up to 100G of data, while small access devices are limited to 10G. Node capacity is directly proportional to the node's initial load, defined as follows:

[0092] C i = (1+theta)L i , i∈(1,N);

[0093] Where C i Represents node v i The capacity is denoted by theta, which is the capacity backup factor, also known as the redundancy factor, and theta > 0. When theta = 0, the node capacity is the same as the maximum load capacity, meaning the node has no capacity backup.

[0094] Example 3

[0095] Figure 5 This is a flowchart of a network load redistribution method provided in Embodiment 2 of the present invention. Figure 5 As shown, the method includes:

[0096] S510. Calculate the remaining capacity of each neighboring node of the failed network node.

[0097] In the study of network cascading failures, load redistribution of a failed node refers to the process by which the load carried by a node in a network is distributed to its associated nodes through various allocation methods when a node fails. Figure 6This is a schematic diagram of a load redistribution method applicable to Embodiment 3 of the present invention, as shown below. Figure 6 As shown, when node v i After being attacked and rendered ineffective, the original method via v i The load on a node will be re-selected via a different path, and its own load will be transferred to its neighboring nodes to ensure the efficient operation of the entire network.

[0098] S520. Determine the proportion of the remaining capacity of each neighboring node to the total remaining capacity of all neighboring nodes, and use this proportion as the load allocation ratio for that neighboring node.

[0099] S530: Distribute the load of the failed network node to each neighboring node according to the load distribution ratio.

[0100] When a node is in state L in the network, it is considered invalid and will be removed from the network. The network traffic load it carries will be redistributed to its neighboring nodes through a reallocation strategy Π, and each neighboring node will update its traffic load L′. j When the updated service load L j ′>C j Network node j will cascade into failure, its state will change to L, and a new round of service reallocation will begin. If the updated service load is L... j '≤C j Node j will be unaffected.

[0101] According to the failure node v i The remaining capacity of a neighboring node will distribute the load to its neighboring node v. j The ratio is:

[0102]

[0103] Failed node v i neighbor node v j The additional load that should be allocated is:

[0104]

[0105] Where, ΔL ji Indicates the failed node v i Load redistribution on the node v to its neighbor node v j Additional load on L. i It is the failed node v i The load, C j Represents node v j The capacity, L i Indicates v j The initial load, Γ i Represents node v iThe set of neighboring nodes. At this point, after load redistribution, v j The load is:

[0106] L j ′=L j +ΔL ji ;

[0107] If L j '≤C j Then node v j Real-time load is L j ′;If L j ′>C j Then node v j It will fail due to overload; at this moment, node v j The load on will be redistributed to v following the steps described above. j The neighboring nodes of a node.

[0108] Example 4

[0109] The following detailed explanation of the solutions of the various embodiments of the present invention is provided through a specific example, namely, Embodiment 4:

[0110] The experimental data uses SPN network topology data from region X, collecting node data from 10 transmission network elements as the subjects of study. The network has 983 edges, an average path length of 3.0203, a clustering coefficient of 0.25826, and an average node degree of 3.832. The network's carrying capacity is used as the initial load, and the maximum capacity of the SPN network element interface is used as the incoming network capacity.

[0111] This experiment involves four evaluation parameters: attack scaling (Attacked_nodes), tolerance coefficient (CurrentLoad), redundancy coefficient (theta), and robustness (G), where G is the final result evaluation metric. First, three controllable parameters—attack scaling, tolerance coefficient, and redundancy coefficient—are set. Then, any two parameters are controlled, and the network robustness is observed by iterating over one of them.

[0112] Experiment 1: Observe the metrics of network cascading failure robustness under different attack scales (Attacked_nodes).

[0113] The parameter settings are shown in the table below. A total of 5 sets of experiments were conducted. In each set, CurrentLoad and theta were kept constant, and Attacked_nodes were iterated with a step size of 0.2.

[0114]

[0115] When theta = 1, CurrentLoad = 0.8, and Attacked_nodes = 1, the number of attacking nodes reaches approximately 44% of the total number of nodes, and the network damage exceeds 50%. The network begins to collapse when the number of attacked nodes reaches 54.4%. When CurrentLoad = 0.2 and Attacked_nodes = 1, approximately 98.4% of the attacking nodes are required for the network to completely collapse; at this point, the network exhibits good resilience against cascading failures. When Attacked_nodes ∈ [0, 0.8], regardless of the values ​​of the redundancy and tolerance parameters, the network damage exceeds 5% when the number of attacking nodes reaches approximately 0.95%; the network begins to collapse when the number of attacked nodes reaches approximately 5.45%. At this point, the network exhibits weak resilience and poor robustness.

[0116] When theta = 0 and CurrentLoad = 0.8, regardless of the attack scale value, attacking only about 0.6% of the nodes will cause the network to be damaged by more than 50%. When the number of attacked nodes reaches only 1% of the total number of nodes, the network will completely collapse. At this point, the network exhibits very weak robustness; attacking only a small portion of the nodes in the network is enough to cause the network to crash and become unable to function normally.

[0117] When CurrentLoad = 0.5 and Attacked_nodes = 1, with theta = 0, the number of attacked nodes reaches approximately 32%, at which point the network damage exceeds 50%, and attacking just one more node would cause the network to completely collapse. However, when theta = 0.5, the number of attacked nodes reaches approximately 78.8% before the network is completely destroyed, indicating good robustness and resistance to damage. When Attacked_nodes ∈ [0, 0.8], regardless of the values ​​of the tolerance and redundancy parameters, the network begins to completely collapse when the number of attacked nodes reaches 4.6%, indicating weak resistance to cascading failures and poor robustness.

[0118] Conclusion 1: When Attacked_nodes ∈ [0, 0.8], an attack on only a small fraction of nodes can cause network collapse, or even complete collapse. However, when Attacked_nodes = 1, the attack strategy is completely random; the network will only collapse when at least 32.2% of the nodes are attacked, and at most 98.8% of the network nodes must be attacked to cause serious damage. Therefore, the robustness of random attacks shows a significant upward trend with increasing attack scale; while under deliberate attacks, cascading failure networks exhibit weaker robustness.

[0119] Experiment 2: Observe the robustness index of network cascading under different allowable parameters theta.

[0120] Experiments were conducted to observe the robustness of network cascading failures under different attack scales (Attacked_nodes).

[0121] The parameter settings are shown in the table below. A total of 6 sets of experiments were conducted. In each set, Attacked_nodes and CurrentLoad were kept constant, and theta was iterated with a step size of 0.2.

[0122]

[0123] When Attacked_nodes = 1, CurrentLoad = 0.8, and theta = 1, the network damage exceeds 50% when the number of attacked nodes reaches 45.4% of the total number of nodes. Based on this, attacking just one or two more nodes will trigger a complete network failure; that is, the network will completely collapse when the number of attacked nodes reaches 45.8% of the total number of nodes. With CurrentLoad = 0.5, the network damage exceeds 50% when the number of attacked nodes reaches 47%, and the network begins to completely collapse when the number of attacked nodes reaches 94.2%. At this point, the network exhibits good robustness against cascading failures. When theta = 0, the network completely collapses with only 24.4% and 45.5% of the number of attacked nodes, respectively.

[0124] When Attacked_nodes=0 and theta=0, it is clear that regardless of the value of the redundancy parameter, when approximately 1.9% of the nodes are attacked, the network is damaged by more than 50%, and when 4.6% of the nodes are attacked, the network is completely destroyed and cannot function. At this point, the network's robustness against cascading failures is poor.

[0125] Conclusion 2: When the permissible parameters change, the network exhibits extremely weak robustness under deliberate attacks and also shows relatively weak robustness under random attacks, making the network susceptible to damage. However, as the permissible parameters increase, the network's robustness improves.

[0126] Experiment 3: Observe the robustness index of network cascading failure under different redundancy parameters Current_Loads.

[0127] The parameter settings are shown in the table below. A total of 6 sets of experiments were conducted. In each set, Attacked_nodes and theta were kept constant, and CurrentLoad was iterated with a step size of 0.2.

[0128]

[0129] When theta is 1, Attacked_nodes = 0.8, Current_Loads ∈ [0,1]. Attacking approximately 1% of the nodes results in over 50% network damage, and attacking approximately 5.8% of the nodes results in over 90% network collapse. When Attacked_nodes = 0, attacking 0.8% of the total nodes results in over 50% network damage, and attacking 4.1% of the total nodes results in over 90% network collapse. In these cases, the network exhibits extremely weak resilience and very weak robustness against cascading failures.

[0130] When theta = 0 and Attacked_nodes = 0.8, with Current_Loads ∈ [0, 0.8], attacking 1.4% of the nodes results in over 50% network damage. With Current_Loads = 1, attacking only 0.2% of the nodes causes the network to completely collapse. When theta = 0.5, with Current_Loads ∈ [0, 0.8], attacking 1.4% to 3.8% of the nodes causes over 50% network damage. With Current_Loads = 1, attacking only 1% of the nodes causes the network to completely collapse. At this point, the network exhibits extremely poor robustness.

[0131] Conclusion 3: Under varying redundancy parameters, the network exhibits extremely weak robustness. In particular, when the network redundancy is small, attacking only a small portion of the nodes can bring the network to the brink of collapse.

[0132] Figure 7 This is a schematic diagram illustrating the analysis of the destructive impact of controllable parameters applicable to Embodiment 4 of the present invention. According to the above analysis results, Attacked_nodes cause the greatest damage to the network; that is, if core nodes are attacked, the network will experience large-scale cascading failures, resulting in poor destructive resilience. Secondly, the backup of the network SPN board is also a crucial link in ensuring the network is running, requiring sufficient configuration of network backup nodes. Finally, during actual network operation, the node load should be below 80%; if it is too high, the network is prone to triggering cascading failures.

[0133] Example 5

[0134] Figure 8 This is a schematic diagram of the structure of an SPN network resilience assessment device provided in Embodiment 5 of the present invention. Figure 8 As shown, the device includes:

[0135] The model building unit 810 is used to build an SPN network initialization model based on the actual SPN network operation status of the area to be evaluated. The SPN network initialization model includes network nodes that represent the actual deployed transmission network elements in the SPN network and network structures that represent the IP routing connection relationships of each network node.

[0136] The first setting unit 820 is used to set the node load and node capacity of each network node;

[0137] The second setting unit 830 is used to set at least one simulated attack strategy for the SPN network initialization model;

[0138] The simulated attack unit 840 is used to attack network nodes in the SPN network initialization model based on the simulated attack strategy, distribute the node load of the network nodes that fail after the attack to other network nodes, and monitor whether the redistributed network nodes are overloaded and fail until no new network nodes fail.

[0139] Evaluation unit 850 is used to determine resilience indicators and perform resilience assessment based on the number of non-failed nodes and the total number of network nodes in the SPN network initialization model.

[0140] Optionally, the first setting unit 820 is specifically used for execution:

[0141] The service load data of each transmission network element in the actual operation of the SPN network is used as the initial load of each network node; an allowable coefficient is set to characterize the periodic fluctuation characteristics of the load, and the range of the allowable coefficient characterizes the fluctuation range of the network load based on the initial load; the node load of each network node is set according to the initial load and the allowable coefficient.

[0142] The maximum nominal capacity of each transmission network element hardware interface is used as the capacity baseline value for each network node; a redundancy coefficient is set to characterize the degree of capacity backup, and the value of the redundancy coefficient is related to the backup configuration of the network node; the node capacity of each network node is set according to the capacity baseline value and the redundancy coefficient.

[0143] Optionally, the types of simulated attack strategies include: random attacks, deliberate attacks, and mixed attacks, wherein:

[0144] Random attack is used to randomly select and remove network nodes in the SPN network using a random number generation algorithm. The proportion of network nodes removed in each attack to the total number of network nodes is a preset value.

[0145] Intentional attacks are used to sort network nodes in the SPN network from largest to smallest based on their key indicator values, and to prioritize the removal of network nodes that are ranked higher.

[0146] Hybrid attacks are used to adjust the combination ratio of random attacks and deliberate attacks through attack ratio parameters. The number of network nodes for random attacks and the number of network nodes for deliberate attacks are calculated based on the combination ratio. Network nodes are selected by random selection and index sorting respectively, and then merged and removed.

[0147] Optionally, the simulated attack unit 840, when distributing the node load of the attacked and disabled network node to other network nodes, specifically performs the following:

[0148] Calculate the remaining capacity of each neighbor node of the failed network node;

[0149] Determine the proportion of each neighbor node's remaining capacity to the total remaining capacity of all neighbor nodes, and use this proportion as the load allocation ratio for that neighbor node.

[0150] The load of a failed network node is distributed to its neighboring nodes according to the load distribution ratio.

[0151] Optionally, evaluation unit 850, when determining resilience metrics based on the number of non-failed nodes and the total number of network nodes in the SPN network initialization model, specifically performs the following:

[0152] Perform connectivity analysis on the set of non-failed network nodes, and select the largest connected subgraph that is interconnected by effective connection edges. The number of network nodes contained in the subgraph is the number of effective nodes among the non-failed nodes that can realize business transmission.

[0153] The ratio of the number of network nodes in the largest connected subgraph to the total number of network nodes is used to calculate the resilience index.

[0154] Optionally, evaluation unit 850, when performing a survivability assessment, specifically performs the following:

[0155] Determine the target value range of the damage resistance index. Each value range corresponds to a different level of damage resistance. Determine the damage resistance of the area to be evaluated based on the target value range.

[0156] Optionally, evaluation unit 850 is also used to perform:

[0157] Based on resilience, SPN networks are optimized for resilience, including one or more of the following methods: real-time status monitoring of core network nodes and triggering alarms when indicators are abnormal; capacity backup is configured for critical network nodes; and the actual load of network nodes is controlled through traffic scheduling.

[0158] The SPN network resilience assessment device provided in this embodiment of the invention can execute the SPN network resilience assessment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0159] Example 6

[0160] Figure 9 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0161] like Figure 9 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0162] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0163] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the resilience assessment method for SPN networks.

[0164] In some embodiments, the SPN network survivability assessment method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the SPN network survivability assessment method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the SPN network survivability assessment method by any other suitable means (e.g., by means of firmware).

[0165] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0166] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0167] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0168] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0169] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0170] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0171] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0172] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for invulnerability evaluation of a SPN network, characterized in that, The method comprises: constructing an SPN network initialization model based on the actual SPN network operation state of the region to be evaluated, the SPN network initialization model comprising network nodes representing the transmission network elements actually deployed in the SPN network and network structures representing the IP routing connection relationships between the network nodes; setting the node load and node capacity of each network node; setting at least one simulated attack strategy for the SPN network initialization model; attacking the network nodes in the SPN network initialization model based on the simulated attack strategy, distributing the node load of the network nodes failed after the attack to other network nodes, and monitoring whether the network nodes after the redistribution are overloaded and failed until no new network node is failed; determining the invulnerability index based on the number of nodes not failed in the SPN network initialization model and the total number of network nodes and performing invulnerability evaluation.

2. The method of claim 1, wherein, The setting of the node load and node capacity of each network node comprises: using the service load data of each transmission network element in the actual operation of the SPN network as the initial load of each network node; setting an allowable coefficient representing the periodic fluctuation characteristics of the load, the value range of the allowable coefficient representing the fluctuation amplitude of the network load based on the initial load; setting the node load of each network node according to the initial load and the allowable coefficient; using the maximum nominal capacity of the hardware interface of each transmission network element as the capacity reference value of each network node; setting a redundancy coefficient representing the backup degree of the capacity, the value of the redundancy coefficient being related to the backup configuration of the network node; setting the node capacity of each network node according to the capacity reference value and the redundancy coefficient.

3. The method of claim 1, wherein, The types of the simulated attack strategy include random attack, deliberate attack and mixed attack, wherein: The random attack is used to randomly select and remove network nodes of the SPN network through a random number generation algorithm, and the proportion of the number of network nodes removed each time to the total number of network nodes is a preset value; The deliberate attack is used to sort the network nodes in the SPN network according to the key indicator values of all the network nodes from large to small, and preferentially select the network nodes at the top of the sorting for removal; The mixed attack is used to control the combination proportion of the random attack and the deliberate attack through an attack proportion parameter, calculate the number of network nodes for random attack and the number of network nodes for deliberate attack according to the combination proportion, and remove the network nodes selected through random selection and index sorting, respectively, and then combine and remove them.

4. The method of claim 1, wherein, The distribution of the node load of the network nodes failed after the attack to other network nodes comprises: calculating the remaining capacity of each neighbor node of the failed network node; determining the proportion of the remaining capacity of each neighbor node to the total sum of the remaining capacities of all the neighbor nodes as the load distribution proportion of the neighbor node; distributing the load of the failed network node to each neighbor node according to the load distribution proportion.

5. The method of claim 1, wherein, The determination of the invulnerability index based on the number of nodes not failed in the SPN network initialization model and the total number of network nodes comprises: The connectivity of the set of non-failed network nodes is analyzed, and a largest connected subgraph that is connected to each other through effective connection edges is screened out from the set of non-failed network nodes, and the number of network nodes in the largest connected subgraph is the number of effective nodes that can realize service interconnection among the non-failed nodes; The number of network nodes in the largest connected subgraph is compared with the total number of network nodes to obtain the result as the invulnerability index.

6. The method of claim 1, wherein, The invulnerability evaluation includes: Determining a target value interval to which the invulnerability index belongs, each value interval corresponding to a different level of invulnerability, and determining the invulnerability of the to-be-evaluated area according to the target value interval.

7. The method of claim 6, wherein, The method further includes: According to the invulnerability, the invulnerability optimization of the SPN network is performed, including one or more of the following modes: real-time state monitoring of core network nodes, alarm triggered when the index is abnormal; key network node capacity backup; and actual load control of network nodes through traffic scheduling.

8. A survivability assessment device for an SPN network, characterized in that, The device includes: A model construction unit configured to construct an SPN network initialization model based on the actual SPN network running state of the to-be-evaluated area, the SPN network initialization model including network nodes corresponding to transmission network elements actually deployed in the SPN network and network structures representing IP routing connection relationships between the network nodes; A first setting unit configured to set node loads and node capacities of the network nodes; A second setting unit configured to set at least one simulation attack strategy for the SPN network initialization model; A simulation attack unit configured to attack the network nodes in the SPN network initialization model based on the simulation attack strategy, to allocate node loads of failed network nodes to other network nodes after the attack, and to monitor whether the network nodes are overloaded and failed after the reallocation until no new network node is failed; An evaluation unit configured to determine an invulnerability index based on the number of non-failed nodes and the total number of network nodes in the SPN network initialization model and to perform invulnerability evaluation.

9. An electronic device, comprising: The electronic device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the invulnerability evaluation method of the SPN network according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the invulnerability evaluation method of the SPN network according to any one of claims 1-7 when executed.

11. A computer program product, characterised in that, The computer program product includes a computer program that, when executed by a processor, implements the invulnerability evaluation method of the SPN network according to any one of claims 1-7.

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