Method, system and device for identifying communication network bottlenecks based on mcfp

Abstract

The application relates to a communication network bottleneck identification method, system and equipment based on MCFP, and belongs to the technical field of network communication. The method comprises the following steps: constructing an undirected graph of a communication network topology; defining a service demand for each pair of nodes in the undirected graph, and assigning a capacity value to each edge to construct an MCFP model; constructing a dual model of the MCFP model based on the Lagrange dual theory, synchronously solving the dual models by using a column generation algorithm, evaluating the network load bearing capacity based on the maximum concurrency factor obtained by solving, quantitatively identifying and classifying the key bottleneck links in the network based on the capacity constraint optimal dual variable obtained by solving, and finally realizing accurate identification of the communication network load and the bottleneck. The application can provide direct and reliable decision guidance for scientific allocation of network resources and link expansion optimization.

Description

Technical Field

[0001] This application relates to the field of network communication technology, and in particular to a method, system and device for identifying communication network bottlenecks based on MCFP. Background Technology

[0002] In modern communication scenarios such as industrial automation bus networks, cloud computing data center networks, and Internet of Things communication networks, the surge in the number of access devices and the explosive growth in business data volume have made the accurate identification of network load and performance bottlenecks a core challenge that restricts data transmission efficiency and affects the stability of business operations.

[0003] Existing network load capacity assessment technologies are mostly limited to single-link traffic statistics or empirical judgments, failing to comprehensively consider the global impact of network topology, link capacity correlation, and service demand distribution. This can lead to excessive resource reservation during network planning, resulting in resource waste, or performance bottlenecks due to insufficient resource allocation, making it difficult to accurately match network resources with service needs. At the bottleneck identification level, existing technologies largely rely on traffic threshold triggering or post-fault backtracking analysis, generally suffering from high false positive rates, strong lag, and an inability to quantify the degree of bottleneck impact. Specifically, on the one hand, some links may have high traffic but are not critical to network performance; over-focusing on such links can lead to resource misallocation. On the other hand, some links may have traffic below preset thresholds but are the only links connecting core areas—"structural bottlenecks"—and ignoring them will lead to long-term latent bottleneck risks. Furthermore, existing technologies cannot quantify the impact weight of each link on overall network performance, making it difficult to distinguish between critical bottlenecks and general congestion. This results in a lack of targeted network link expansion and optimization solutions, affecting the improvement of communication network data transmission efficiency. Summary of the Invention

[0004] Therefore, it is necessary to provide a communication network bottleneck identification method, system, and device based on MCFP to address the aforementioned technical problems.

[0005] A bottleneck identification method for communication networks based on MCFP, the method comprising:

[0006] By abstracting the physical devices in the communication network as a set of nodes and the physical links between devices as a set of edges, an undirected graph of the network topology is constructed.

[0007] The MCFP model is constructed by defining business requirements for each pair of nodes in an undirected graph and assigning a capacity value to each edge. The MCFP model is configured to search for the optimal path traffic while satisfying business requirement constraints and link capacity constraints, in order to maximize the concurrency factor, which is defined as the network load carrying capacity coefficient.

[0008] A dual model of the MCFP model is constructed based on Lagrange duality theory. A column generation algorithm is used to solve the two models simultaneously. The network load carrying capacity is evaluated based on the maximum concurrency factor obtained from the solution. The key bottleneck links in the network are quantitatively identified and classified based on the optimal dual variables of capacity constraints obtained from the solution. Finally, the accurate identification of communication network load and bottlenecks is achieved.

[0009] In one embodiment, the method further includes, before constructing the MCFP model:

[0010] In an undirected graph of a communication network topology In the definition, the capacity function and the business requirement function are defined; among them, For a set of nodes, Let be the set of edges. The total number of nodes. For the first Link, , Total number of links;

[0011] The capacity function is defined as The capacity function Link The capacity is defined as , The value is determined based on the physical characteristics of the link, protocol standards, or actual test data; among which, It is the set of nonnegative real numbers;

[0012] Business requirement function is defined as ,satisfy and ;in, Represents a node With nodes Average service transmission requirements The value is determined by historical traffic statistics, business forecasts, or business specification requirements;

[0013] Collect the capacity from the capacity function and the business requirements from the business requirements function, and perform preprocessing.

[0014] In one embodiment, the capacity in the capacity function and the business requirements in the business requirement function are collected and preprocessed, including:

[0015] Outlier handling: using The criteria identify outliers in link capacity and replace them with the average capacity of links of the same type or remove them.

[0016] Demand smoothing: The sliding window averaging method is used to eliminate the impact of short-term traffic pulses on the business demand function. The size of the sliding window is set according to the business cycle.

[0017] Unit unification: The units for link capacity and service requirements are uniformly converted to Mbps.

[0018] In one embodiment, an MCFP model is constructed by defining business requirements for each pair of nodes in the undirected graph and assigning a capacity value to each edge, expressed as:

[0019] ;

[0020] in, Concurrency factor; Path in the network Traffic allocation on the platform; As a constraint of business requirements, it represents the condition for a set of nodes. Any pair of nodes in The sum of traffic on all paths equals the average service transmission demand. The product of the concurrency factor, For nodes With nodes The set of all paths between; Link capacity constraint, meaning that for a set of edges any link in All passing through the link The sum of path traffic does not exceed the link capacity , For included links The set of all paths; and The non-negativity constraint indicates that both path flow and concurrency factor are non-negative.

[0021] In one embodiment, the dual model of the MCFP model, constructed based on Lagrange duality theory, is expressed as follows:

[0022] ;

[0023] in, For link Capacity-constrained dual variables; For node pairs with communication needs The dual variables of the demand constraints between them; This is a function for business requirements.

[0024] In one embodiment, a column generation algorithm is used for simultaneous solution of the two models, including:

[0025] Constructing the Restricted Master Problem (RMP): For each pair of average traffic transmission requirements node pairs Generate initial paths to form an initial path set. ;based on Construct an RMP, at which point the RMP variables only contain Path flow and concurrency factor The constraints of RMP are consistent with those of the MCFP model.

[0026] Solving RMP: The interior-point method is used to solve RMP and obtain the initial path flow. Initial concurrency factor Demand constraint dual variables and capacity-constrained dual variables ;

[0027] Pricing sub-problems and finding improvement paths: Defining link weights For each pair node pairs Solve Shortest path with weight The path weight is the sum of the weights of the links contained in the path.

[0028] Calculate the shortest path The test number is When the test number is less than 0, This will improve the concurrency factor. The value, and Added to the path set as an improved path If the test count for all paths is greater than or equal to 0, then the solution process is terminated.

[0029] Iterative update: Adding improvement paths Update the RMP and solve it again. Repeat the RMP solution steps and the steps of pricing the subproblem and finding an improved path until there are no paths with a test number less than 0. Stop the calculation and obtain the optimal solution of the MCFP model and the optimal solution of the dual model. Among them, the optimal solution of the MCFP model includes the maximum concurrency factor. Optimal path traffic and optimal link traffic The optimal solution of the dual model includes the optimal dual variables of the demand constraint. and capacity-constrained optimal dual variables ;in, For included links The set of all paths.

[0030] In one embodiment, the key bottleneck links within the network are quantitatively identified and classified based on the solved capacity-constrained optimal dual variables, including:

[0031] When link of At that time, the link The link was identified as a critical bottleneck; among them, when the link of and At that time, critical bottleneck links are classified as strong bottleneck links; when the link of and At that time, critical bottleneck links are classified into potential bottleneck links; among them, and They represent the capacity-constrained optimal dual variables respectively. The upper and lower bounds; when the optimal solution obtained by the model is unique. ;

[0032] when At that time, the link The link is determined to be non-critical; non-critical links are classified as links that are fully loaded in some optimal solutions but not fully loaded in other optimal solutions, or links that are not fully loaded in all optimal solutions.

[0033] A communication network bottleneck identification system based on MCFP, the system comprising:

[0034] The network parameter modeling module is used to construct an undirected graph of the network topology by abstracting physical devices in the communication network as a set of nodes and physical links between devices as a set of edges.

[0035] The MCFP model building module is used to construct an MCFP model by defining business requirements for each pair of nodes in an undirected graph and assigning a capacity value to each edge. The MCFP model is configured to search for the optimal path traffic while satisfying business requirement constraints and link capacity constraints, in order to maximize the concurrency factor, where the concurrency factor is defined as the network load carrying capacity coefficient.

[0036] The bottleneck identification module is used to construct the dual model of the MCFP model based on Lagrange duality theory. It uses a column generation algorithm to solve the dual model synchronously, evaluates the network load carrying capacity based on the maximum concurrency factor obtained from the solution, and quantitatively identifies and classifies key bottleneck links in the network based on the optimal dual variables of capacity constraints obtained from the solution, ultimately achieving accurate identification of communication network load and bottlenecks.

[0037] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0038] By abstracting the physical devices in the communication network as a set of nodes and the physical links between devices as a set of edges, an undirected graph of the network topology is constructed.

[0039] The MCFP model is constructed by defining business requirements for each pair of nodes in an undirected graph and assigning a capacity value to each edge. The MCFP model is configured to search for the optimal path traffic while satisfying business requirement constraints and link capacity constraints, in order to maximize the concurrency factor, which is defined as the network load carrying capacity coefficient.

[0040] A dual model of the MCFP model is constructed based on Lagrange duality theory. A column generation algorithm is used to solve the two models simultaneously. The network load carrying capacity is evaluated based on the maximum concurrency factor obtained from the solution. The key bottleneck links in the network are quantitatively identified and classified based on the optimal dual variables of capacity constraints obtained from the solution. Finally, the accurate identification of communication network load and bottlenecks is achieved.

[0041] Compared with existing technologies, the above-mentioned communication network bottleneck identification method, system, and equipment based on MCFP have the following advantages:

[0042] 1. Based on the undirected graph of network topology, a Maximum Concurrent Flow Problem (MCFP) model is established and solved. This model deeply integrates the global coupling relationship between network topology, link capacity matrix and service demand matrix, and provides a network performance index that transcends local observation, is accurate and can be repeatedly computed, and provides a solid theoretical foundation for network planning, capacity pre-assessment and performance bottleneck early warning.

[0043] 2. This innovative approach applies the duality theory of linear programming, transforming bottleneck identification into an adjunct output of an optimization problem. The maximum concurrency factor obtained from the model solution accurately assesses the upper limit of network load capacity, while the optimal dual variable for capacity constraints, physically representing the shadow price of link capacity, becomes an objective and rigorous mathematical criterion for judging critical bottlenecks, completely eliminating subjective arbitrariness. More importantly, the calculated values ​​of the optimal dual variable for capacity constraints directly and quantitatively characterize the "marginal value" of each link to global performance, achieving precise quantitative identification and classification of the importance of bottleneck links in the communication network. This provides direct and reliable decision-making guidance for the scientific allocation of network resources and link expansion optimization, thereby effectively improving the data transmission efficiency of the communication network and enhancing the stability and reliability of service operations. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating a communication network bottleneck identification method based on MCFP in one embodiment;

[0045] Figure 2 This is a block diagram of a communication network bottleneck identification system based on MCFP in one embodiment;

[0046] Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0048] In one embodiment, such as Figure 1 As shown, a bottleneck identification method for communication networks based on MCFP is provided, including the following steps:

[0049] Step 1: By abstracting the physical devices in the communication network into a set of nodes and the physical links between devices into a set of edges, an undirected graph of the network topology is constructed.

[0050] Step 2: By defining business requirements for each pair of nodes in the undirected graph and assigning a capacity value to each edge, the MCFP model is constructed. The MCFP model is configured to search for the optimal path traffic while satisfying business requirement constraints and link capacity constraints, in order to achieve the goal of maximizing the concurrency factor, where the concurrency factor is defined as the network load carrying capacity coefficient.

[0051] Step 3: Construct the dual model of the MCFP model based on Lagrange duality theory, use the column generation algorithm to solve the dual model simultaneously, evaluate the network load carrying capacity based on the maximum concurrency factor obtained from the solution, and quantitatively identify and classify the key bottleneck links in the network based on the optimal dual variables of the capacity constraints obtained from the solution, so as to achieve accurate identification of communication network load and bottlenecks.

[0052] The aforementioned MCFP-based bottleneck identification method for communication networks can quantify the load ceiling and bottleneck impact from the overall network perspective, enabling accurate assessment of communication network load and quantitative identification of key bottlenecks. This provides direct and reliable decision guidance for the scientific allocation of network resources and link expansion optimization, which is conducive to improving the data transmission efficiency of communication networks and enhancing the stability and reliability of service operation.

[0053] In one embodiment, the purpose of step 1 is to transform the physical characteristics of the actual communication network into standardized parameters required by the MCFP model, providing an input basis for subsequent solutions. The core is to define the mathematical expression of network topology, link capacity, and service requirements, which specifically includes the following steps:

[0054] Step 1.1, Network Topology and Parameter Definition: Abstract the physical devices (routers, switches, terminal nodes, servers) in the communication network into a set of nodes. , Given the total number of nodes, the physical links between devices (fiber optic, wireless, bus links) are abstracted as a set of edges. , For the first Link, , Construct an undirected graph of the network topology to represent the total number of links. For example, in an industrial automation bus network, the master controller and slave devices are nodes, and the bus between the master and slave devices is an edge; in a cloud computing data center network, the core switch and server are nodes, and the optical fiber between the switches is an edge.

[0055] In an undirected graph of a communication network topology In this context, capacity functions and business requirement functions are defined.

[0056] The capacity function is defined as The capacity function Link The capacity is defined as (Unit: Mbps / Gbps) The value is determined based on the physical characteristics of the link (such as fiber bandwidth and wireless channel capacity), protocol standards (such as Ethernet IEEE 802.3 and industrial bus PROFINET), or actual test data; among which, It is the set of non-negative real numbers.

[0057] Business requirement function is defined as ,satisfy (Two-way communication services) and (Nodes have no self-requirements); among them, Represents a node With nodes The average service transmission demand (unit: Mbps) can be obtained in three ways: ① Historical traffic statistics (collecting average values ​​for 1-3 months); ② Service forecasting (ARIMA (Autoregressive Integral Moving Average) / LSTM (Long Short-Term Memory Neural Network) to predict growth trends); ③ Service specification requirements (such as the explicit bandwidth requirements of industrial control instructions and medical imaging).

[0058] Step 1.2: To ensure the accuracy of the input to the MCFP model, the capacity in the capacity function and the business requirements in the business requirements function are further collected and preprocessed.

[0059] In one embodiment, step 1.2 specifically includes:

[0060] Outlier handling: using The criteria identify outliers in link capacity (such as exceeding physical limits due to testing errors), replacing them with the average capacity of similar links or removing them entirely. Demand smoothing: A sliding window averaging method is used to eliminate the impact of short-term traffic spikes on the service demand function; the sliding window size is set according to the service cycle. Unit standardization: The units for link capacity and service demand are standardized to Mbps (bits per second) to avoid calculation errors caused by unit differences.

[0061] In one embodiment, step 2 is the core mathematical support of this application, specifically including: the goal of the MCFP model is to maximize the network's concurrency factor Z (quantifying the network's maximum load-bearing capacity) while satisfying service demand constraints and link capacity constraints. The model form is as follows:

[0062] Decision variables:

[0063] (1) Path flow : ( (a set of all paths in the network), representing a path. Traffic allocation (unit: Mbps).

[0064] (2) Concurrency factor Dimensionless, representing the network load capacity coefficient. The larger the network size, the higher the load it can handle while still meeting demand.

[0065] Objective function: Maximize the concurrency factor ,Right now: .

[0066] Constraints:

[0067] (1) Business requirement constraints: For the node set Any pair of nodes in The sum of traffic on all paths equals the average service transmission demand. The product of this and the concurrency factor ensures that business requirements are met. ,in For nodes With nodes The set of all paths between.

[0068] (2) Link capacity constraint: For the edge set any link in All passing through the link The sum of path traffic does not exceed the link Capacity to avoid link overload: ,in For included links The set of all paths.

[0069] (3) Non-negativity constraint: Both path flow and concurrency factor are non-negative, which is consistent with the physical meaning: .

[0070] Based on the above modeling, the MCFP model is constructed, and its mathematical form is as follows:

[0071] .

[0072] In one embodiment, step 3 specifically includes:

[0073] Step 3.1, Construction of the dual model and its theoretical basis.

[0074] (1) The original problem is transformed into a standard minimization linear programming problem.

[0075] To adapt the solution to duality theory, the MCFP edge-path maximization problem is presented ( This can be transformed into a standard minimum linear programming problem:

[0076] Decision variable vector: ,in For the initial path set The number of paths in the data.

[0077] Objective function: , corresponding to the target coefficient vector (All path traffic) The coefficient is 0. The coefficient is -1).

[0078] Constraint matrix form:

[0079] Business requirement constraints: ,in For the demand association matrix (if path Belongs to node pairs If the value is 1, then the element at the corresponding position is 1; otherwise, it is 0. The corresponding element is ), (Zero vector).

[0080] Link capacity constraints: ,in For the capacity incidence matrix (if path Includes links If the value is 1, then the element at the corresponding position is 1; otherwise, it is 0. The corresponding column element is 0). Link capacity vector (elements are...) ).

[0081] Among them, Lagrange duality theory is the basic idea of ​​which is to introduce Lagrange multipliers (dual variables) to integrate the constraints of the original problem into the objective function to form a Lagrange function, and then construct the dual problem by minimizing and maximizing the function.

[0082] (2) Definition and theoretical basis of dual variables.

[0083] Based on the duality theory of linear programming, dual variables are introduced for each of the two types of constraints:

[0084] Demand constraint dual variables : Corresponding demand equality constraint Unsigned (the dual variable of the equality constraint can be positive or negative), representing the implicit contribution of unit business demand to the network's "virtual cost".

[0085] Capacity-constrained dual variables : Corresponding capacity inequality constraint ,satisfy (The dual variable of the inequality constraint “≤” is non-negative), and its physical meaning is the “shadow price” of the link capacity, that is, the degree of influence of each unit increase in capacity on the objective function.

[0086] (3) Construction of dual model.

[0087] Based on the objective function and constraints of the original problem, construct the dual problem:

[0088] Dual objective function: (Minimize the “total virtual capacity cost”, which is the sum of the products of the dual variables of the original problem’s capacity constraint and the capacity.)

[0089] Duality constraint: For node pairs with communication requirements All paths ,satisfy and .

[0090] Dual variable constraints: , No sign restriction.

[0091] The mathematical form of the dual model is:

[0092] .

[0093] Step 3.2, Solving for the optimal solution and deriving the core conclusions.

[0094] (1) Synchronous acquisition of optimal solution and application of strong duality theorem.

[0095] When solving the MCFP model and its dual model using the column generation algorithm, the optimal solutions of the MCFP model and the dual model are obtained simultaneously.

[0096] The optimal solution for the MCFP model is: (Maximum Concurrency Factor) (Optimal path traffic) (Optimal traffic for the link).

[0097] Optimal solution of dual model: (Demand-constrained optimal dual variables) (Capacity-constrained optimal dual variable).

[0098] According to the strong duality theorem, if the primal problem has an optimal solution, then the dual problem also has an optimal solution, and the optimal objective function values ​​of the two problems are equal. For the MCFP problem in this application, the optimal objective function value of the primal problem is... The optimal objective value of the dual problem is Therefore:

[0099] ;

[0100] This formula shows that the maximum load-carrying capacity of a network is determined by the sum of the products of the capacity of each link and its "shadow price" (the capacity-constrained optimal dual variable). It directly reflects the weight of link capacity in terms of its contribution to the overall network performance.

[0101] (2) Key applications of complementary relaxation.

[0102] The complementary slackness of linear programming states that at the optimal solution, if a constraint of the original problem is a "loose constraint" (i.e., the inequality constraint is strictly true), then its corresponding dual variable is 0; if the dual variable is not 0, then its corresponding constraint of the original problem is a "tight constraint" (i.e., the equality constraint or inequality constraint is equal).

[0103] Applying complementary relaxation to the MCFP problem, specifically addressing capacity constraints. With dual variables ,have: .

[0104] Based on the three categories of links in MCFP (critical edges, saturated non-critical edges, and unsaturated edges), the derivation is performed for each case. The possible values ​​of:

[0105] Critical edge: defined as "a link that saturates under all maximum concurrent flows", i.e. ( (for the optimal flow set) At this point, the capacity constraint is a "tight constraint" ( ), combined with complementary relaxation, It can be non-zero; further combining the strong duality theorem and the envelope theorem, and considering the possibility of multiple solutions, its upper bound can be derived. ,and The value of is positively correlated with the strength of the link's constraint on network performance.

[0106] Saturated non-critical edges: defined as "there exists a partial maximum concurrent flow such that..." There exists a part that makes "Link". For making The optimal flow, with a capacity constraint of "loose constraint" ( In combination with complementary relaxation, it must satisfy .

[0107] Unsaturated edges: defined as "links that are not saturated under all maximum concurrent flows", that is , At this point, the capacity constraint is a "loose constraint" ( ), combined with complementary relaxation, .

[0108] In summary, the core conclusion can be drawn: the link Capacity-constrained upper bound of optimal dual variables The necessary and sufficient condition is This conclusion, which identifies critical edges, provides a rigorous theoretical basis for critical link identification.

[0109] (3) Deepening the physical meaning of dual variables.

[0110] According to the envelope theorem, if a linear programming problem has a unique solution, then the partial derivative of the optimal value of the objective function with respect to a certain parameter is equal to the value of the coefficient of that parameter in the Lagrange function at the dual optimal solution. For the MCFP problem, ... Considered as link capacity The function, for Find the partial derivative: .

[0111] Since linear programming may have multiple optimal solutions, using and Let these represent the upper and lower bounds of the dual variable, respectively. If the solution is unique, then... The above formula can now be restated using the directional derivative as follows:

[0112] ;

[0113] in, The positive directional derivative, It is the negative directional derivative. The vector corresponding to the right-hand side of the capacity constraint (i.e., all) (A vector formed by splicing together) for Unit vector in the direction, For the optimal dual variable set, Let it be "defined as"; from the above equation, it is clear that for the case of a unique optimal solution... This still holds true. Its physical meaning is: link. For every tiny increase / decrease in capacity, the network's maximum concurrency factor decreases. The increment / decrement directly quantifies the marginal impact of changes in link capacity on network performance.

[0114] Step 3.3, Quantitative identification of critical links.

[0115] First, based on The core conclusion is to define a continuous impact factor to achieve accurate identification of critical links.

[0116] The continuous impact factor is the marginal throughput gain. Its definition is to directly take the optimal solution of the dual variable, that is... The calculation method involves directly extracting the optimal solution from the dual problem of the MCFP. No additional calculations are required. The identification rule is: At that time, the link This refers to the critical link. It's important to note that within the critical link, only increasing the capacity of the bottleneck link will improve performance. For potentially bottlenecked links, simply increasing the capacity of that side will not improve performance. The necessary and sufficient condition for a critical link is that reducing the capacity of this edge will inevitably lead to... decline. At that time, link For non-critical links (capacity increase to) No impact).

[0117] Result verification: Referring to MCFP, "after the key edge is deleted..." The property of "inevitable decline" is addressed through "virtual link deletion". Then re-solve MCFP to verify – if deleted ,but As a critical link, with The recognition results are consistent.

[0118] When link of At that time, the link It has been identified as a critical bottleneck link; mathematically proven, This is a necessary and sufficient condition for a link to be fully loaded under all optimal traffic distributions (i.e., traffic strictly equals capacity). This means that such links are the absolute bottleneck restricting network performance improvement. Furthermore, when the link... of and At that time, critical bottleneck links are classified as strong bottleneck links; when the link of and In this context, critical bottleneck links are classified as potential bottleneck links. Strong bottleneck links and potential bottleneck links respectively indicate whether increasing the capacity of only that link can effectively improve network performance. and They represent the capacity-constrained optimal dual variables respectively. The upper and lower bounds; when the optimal solution obtained by the model is unique. .

[0119] when At that time, the link Links are classified as non-critical. Non-critical links are further categorized into those that are fully loaded in some optimal solutions but not fully loaded in others (saturated non-critical edges) or those that are not fully loaded in any optimal solution (unsaturated edges). Expanding the network under these two conditions will not directly increase the maximum concurrency factor for links in the current network state. However, the capacity expansion effect can be achieved through certain combination methods.

[0120] Furthermore, based on the classification and identification of key bottleneck links, it is also possible to further analyze all identified bottleneck links. The key bottleneck link, based on Sort the values ​​in descending order; A higher value indicates a greater marginal contribution of increased link capacity to overall network performance, and it will receive higher expansion priority when resources are limited. During expansion, the capacity of multiple critical bottleneck links, after priority ranking, can be adjusted first, generating several expansion candidate schemes with different adjustment amounts. Then, the adjusted link capacity of each expansion candidate scheme is re-substituted into the MCFP model for solution, obtaining the updated maximum concurrency factor. This updated maximum concurrency factor is compared with the original maximum concurrency factor to obtain the marginal gain in network performance. Finally, by combining the marginal gain in network performance, the impact of link capacity changes, and implementation costs, a comprehensive evaluation of the expansion candidate schemes is conducted, and the optimal expansion scheme is selected for implementation.

[0121] It's important to note that a necessary condition for a capacity expansion scheme to ultimately be effective is that the expanded network must still be able to support all original service demands. This is equivalent to verifying that, under the expanded network capacity configuration, all pairs of nodes with service demands are connected in the subgraph composed of positive-capacity links. This is achieved by obtaining the updated maximum concurrency factor. By comparing with the original maximum concurrency factor, the marginal gain of network performance is obtained. This condition can be naturally verified (if) (If so, it is effective). Finally, by combining the marginal gain of network performance (ΔZ), the rapid prediction results of the impact of link capacity changes, and implementation costs of each expansion candidate scheme, a multi-objective trade-off is made, and the optimal expansion scheme is selected for implementation.

[0122] In one embodiment, since the number of network paths may increase exponentially with the number of nodes, the traditional simplex method is inefficient. Therefore, a column generation algorithm is used to solve the model. Step 3.2 uses the column generation algorithm to solve the dual models simultaneously, including:

[0123] (1) Constructing the Restricted Master Problem (RMP): For each pair of average service transmission requirements node pairs Generate initial paths, for example, by using Dijkstra's algorithm to find the shortest path, where "shortest" is defined as having the fewest links, thus forming an initial path set. ;based on Construct an RMP, at which point the RMP variables only contain Path flow and concurrency factor The constraints of RMP are consistent with those of the MCFP model.

[0124] (2) Solving RMP: The interior point method is used to solve RMP to obtain the initial path flow. Initial concurrency factor Demand constraint dual variables and capacity-constrained dual variables .

[0125] (3) Pricing sub-problem and finding improvement paths: Defining link weights For each pair node pairs Solve Shortest path with weight The path weight is the sum of the weights of the links within the path; the shortest path is calculated. The reduced cost is: When the test number is less than 0, This will improve the concurrency factor. The value, and Added to the path set as an improved path If the test number for all paths is greater than or equal to 0, then the solution process is terminated.

[0126] (4) Iterative update: Add the improvement path Update the RMP and solve it again. Repeat the RMP solution steps and the steps of pricing the subproblem and finding an improved path until there are no paths with a test number less than 0. Stop the calculation and obtain the optimal solution of the MCFP model and the optimal solution of the dual model. Among them, the optimal solution of the MCFP model includes the maximum concurrency factor. (Network maximum load capacity limit), optimal path traffic (Optimal network traffic distribution) and optimal link traffic The optimal solution of the dual model includes the optimal dual variables of the demand constraint. and capacity-constrained optimal dual variables ;in, For included links The set of all paths.

[0127] In one embodiment, such as Figure 2 As shown, a communication network bottleneck identification system based on MCFP is provided, including:

[0128] The network parameter modeling module 201 is used to construct an undirected graph of the network topology by abstracting the physical devices in the communication network as a set of nodes and the physical links between devices as a set of edges.

[0129] The MCFP model building module 202 is used to construct the MCFP model by defining business requirements for each pair of nodes in the undirected graph and assigning a capacity value to each edge. The MCFP model is configured to search for the optimal path traffic while satisfying business requirement constraints and link capacity constraints, so as to achieve the goal of maximizing the concurrency factor, where the concurrency factor is defined as the network load carrying capacity coefficient.

[0130] The bottleneck identification module 203 is used to construct the dual model of the MCFP model based on Lagrange duality theory. It uses a column generation algorithm to solve the dual model synchronously, evaluates the network load carrying capacity based on the maximum concurrency factor obtained from the solution, and quantitatively identifies and classifies the key bottleneck links in the network based on the optimal dual variables of the capacity constraints obtained from the solution, so as to achieve accurate identification of communication network load and bottlenecks.

[0131] Specific limitations regarding the MCFP-based communication network bottleneck identification system can be found in the limitations of the MCFP-based communication network bottleneck identification method described above, and will not be repeated here. Each module in the aforementioned MCFP-based communication network bottleneck identification system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0132] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a communication network bottleneck identification method based on MCFP. The display screen can be an LCD screen or an e-ink display screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0133] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0134] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to perform the following steps:

[0135] By abstracting the physical devices in the communication network as a set of nodes and the physical links between devices as a set of edges, an undirected graph of the network topology is constructed.

[0136] The MCFP model is constructed by defining business requirements for each pair of nodes in an undirected graph and assigning a capacity value to each edge. The MCFP model is configured to search for the optimal path traffic while satisfying business requirement constraints and link capacity constraints, in order to maximize the concurrency factor, which is defined as the network load carrying capacity coefficient.

[0137] A dual model of the MCFP model is constructed based on Lagrange duality theory. A column generation algorithm is used to solve the two models simultaneously. The network load carrying capacity is evaluated based on the maximum concurrency factor obtained from the solution. The key bottleneck links in the network are quantitatively identified and classified based on the optimal dual variables of capacity constraints obtained from the solution. Finally, the accurate identification of communication network load and bottlenecks is achieved.

[0138] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application.

Claims

1. A bottleneck identification method for communication networks based on MCFP, characterized in that, The method includes: By abstracting the physical devices in the communication network as a set of nodes and the physical links between devices as a set of edges, an undirected graph of the network topology is constructed. The MCFP model is constructed by defining business requirements for each pair of nodes in an undirected graph and assigning a capacity value to each edge. The MCFP model is configured to search for the optimal path traffic while satisfying business requirement constraints and link capacity constraints, in order to maximize the concurrency factor, which is defined as the network load carrying capacity coefficient. A dual model of the MCFP model is constructed based on Lagrange duality theory. A column generation algorithm is used to solve the two models simultaneously. The network load carrying capacity is evaluated based on the maximum concurrency factor obtained from the solution. The key bottleneck links in the network are quantitatively identified and classified based on the optimal dual variables of capacity constraints obtained from the solution. Finally, the accurate identification of communication network load and bottlenecks is achieved.

2. The communication network bottleneck identification method based on MCFP according to claim 1, characterized in that, Before constructing the MCFP model, the method also includes: In an undirected graph of a communication network topology In the definition, the capacity function and the business requirement function are defined; among them, For a set of nodes, Let be the set of edges. The total number of nodes. For the first Link, , Total number of links; The capacity function is defined as The capacity function in the first Link The capacity is defined as , The value is determined based on the physical characteristics of the link, protocol standards, or actual test data; among which, It is the set of nonnegative real numbers; The business requirement function is defined as follows: ,satisfy and ;in, Represents a node With nodes Average service transmission requirements The value is determined by historical traffic statistics, business forecasts, or business specification requirements; Collect the capacity from the capacity function and the business requirements from the business requirement function, and perform preprocessing.

3. The communication network bottleneck identification method based on MCFP according to claim 2, characterized in that, Collect the capacity from the capacity function and the business requirements from the business requirement function, and perform preprocessing, including: Outlier handling: using The criteria identify outliers in link capacity and replace them with the average capacity of links of the same type or remove them. Demand smoothing: The sliding window averaging method is used to eliminate the impact of short-term traffic pulses on the business demand function. The size of the sliding window is set according to the business cycle. Unit unification: The units for link capacity and service requirements are uniformly converted to Mbps.

4. The communication network bottleneck identification method based on MCFP according to claim 2 or 3, characterized in that, By defining business requirements for each pair of nodes in an undirected graph and assigning a capacity value to each edge, an MCFP model is constructed, expressed as: ； in, Concurrency factor; Path in the network Traffic allocation on the platform; As a constraint of business requirements, it represents the condition for a set of nodes. Any pair of nodes in The sum of traffic on all paths equals the average service transmission demand. The product of the concurrency factor, For nodes With nodes The set of all paths between; Link capacity constraint, meaning that for a set of edges any link in All passing through the link The sum of path traffic does not exceed the link capacity , For included links The set of all paths; and The non-negativity constraint indicates that both path flow and concurrency factor are non-negative.

5. The communication network bottleneck identification method based on MCFP according to claim 4, characterized in that, The dual model of the MCFP model, constructed based on Lagrange duality theory, is expressed as: ； in, For link Capacity-constrained dual variables; For node pairs with communication needs The dual variables of the demand constraints between them; This is a function for business requirements.

6. The communication network bottleneck identification method based on MCFP according to claim 5, characterized in that, A column generation algorithm is used for simultaneous solution of the two models, including: Constructing the Restricted Master Problem (RMP): For each pair of average traffic transmission requirements node pairs Generate initial paths to form an initial path set. ;based on Construct an RMP, at which point the RMP variables only contain Path flow and concurrency factor The constraints of RMP are consistent with those of the MCFP model. Solving RMP: The interior-point method is used to solve RMP and obtain the initial path flow. Initial concurrency factor Demand constraint dual variables and capacity-constrained dual variables ; Pricing sub-problems and finding improvement paths: Defining link weights For each pair node pairs Solve Shortest path with weight The path weight is the sum of the weights of the links contained in the path. Calculate the shortest path The test number is When the test number is less than 0, This will improve the concurrency factor. The value, and Added to the path set as an improved path If the test count for all paths is greater than or equal to 0, then the solution process is terminated. Iterative update: Adding improvement paths Update the RMP and solve it again. Repeat the RMP solution steps and the steps of pricing the subproblem and finding an improved path until there are no paths with a test number less than 0. Stop the calculation and obtain the optimal solution of the MCFP model and the optimal solution of the dual model. Among them, the optimal solution of the MCFP model includes the maximum concurrency factor. Optimal path traffic and optimal link traffic The optimal solution of the dual model includes the optimal dual variables of the demand constraint. and capacity-constrained optimal dual variables ;in, For included links The set of all paths.

7. The communication network bottleneck identification method based on MCFP according to claim 6, characterized in that, Based on the obtained capacity-constrained optimal dual variables, the key bottleneck links within the network are quantitatively identified and classified, including: When link of At that time, the link The link was identified as a critical bottleneck; among them, when the link of and At that time, critical bottleneck links are classified as strong bottleneck links; when the link of and At that time, critical bottleneck links are classified into potential bottleneck links; among them, and They represent the capacity-constrained optimal dual variables respectively. The upper and lower bounds; when the optimal solution obtained by the model is unique. ; when At that time, the link The link is determined to be non-critical; the non-critical link is classified as one that is fully loaded in some optimal solutions but not fully loaded in other optimal solutions, or one that is not fully loaded in all optimal solutions.

8. A communication network bottleneck identification system based on MCFP, characterized in that, The system includes: The network parameter modeling module is used to construct an undirected graph of the network topology by abstracting physical devices in the communication network as a set of nodes and physical links between devices as a set of edges. The MCFP model building module is used to construct an MCFP model by defining business requirements for each pair of nodes in an undirected graph and assigning a capacity value to each edge. The MCFP model is configured to search for the optimal path traffic while satisfying business requirement constraints and link capacity constraints, in order to maximize the concurrency factor, where the concurrency factor is defined as the network load carrying capacity coefficient. The bottleneck identification module is used to construct the dual model of the MCFP model based on Lagrange duality theory. It uses a column generation algorithm to solve the dual model synchronously, evaluates the network load carrying capacity based on the maximum concurrency factor obtained from the solution, and quantitatively identifies and classifies key bottleneck links in the network based on the optimal dual variables of capacity constraints obtained from the solution, ultimately achieving accurate identification of communication network load and bottlenecks.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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