Network switch control method and system

Abstract

The present invention discloses a network switch control method and system, which specifically relate to the field of network traffic management technology, and are used to solve the problem of sudden loss or rearrangement of low-frequency traffic when high-frequency traffic is dynamically compressed in the prior art. By monitoring all traffic entering the network switch, its frequency characteristics are obtained, and the traffic is classified into high-frequency traffic and low-frequency traffic according to the frequency characteristics; a dynamic traffic compression algorithm is applied to compress the high-frequency traffic; for the low-frequency traffic, a sparse representation iterative reweighted least squares method is used to analyze the topological links and identify the link interference strength; the logical coherence of the low-frequency traffic data packets is evaluated by a variational autoencoder; the priority scheduling strategy of the low-frequency traffic is determined based on the link interference strength and logical coherence; finally, resources are allocated and scheduled for the compressed high-frequency traffic and the prioritized low-frequency traffic.

Description

Technical Field

[0001] The present invention relates to the technical field of network traffic management, and more particularly to a network switch control method and system. Background Art

[0002] In modern network communications, network switches, as core devices, are responsible for efficiently forwarding data streams. With the diversification of network applications and the increasing demand for high bandwidth, network traffic has shown complex distribution characteristics, including high-frequency traffic and low-frequency traffic. In actual applications, high-frequency traffic usually occupies a large amount of bandwidth resources. To improve the transmission efficiency of the overall network, dynamic traffic compression technology is usually used in existing technologies to optimize high-frequency traffic. However, low-frequency traffic has real-time and bursty characteristics in some scenarios, and its priority is often ignored, which has caused a series of technical problems.

[0003] When dynamically compressing large-scale high-frequency traffic in the existing technology, the real-time requirements of low-frequency traffic are not fully considered, which may lead to the loss or reordering of low-frequency traffic, thereby affecting the reliability and stability of its transmission. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a network switch control method and system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A network switch control method comprises the following steps:

[0007] Monitor all network traffic entering the network switch, obtain the frequency characteristics of each flow, and classify the network traffic into high-frequency traffic and low-frequency traffic based on the frequency characteristics of the traffic;

[0008] Apply dynamic traffic compression algorithm to compress high-frequency traffic;

[0009] The topological links of low-frequency traffic are analyzed using sparse representation iterative reweighted least squares method to identify the link interference intensity at different levels.

[0010] The variational autoencoder is used to analyze the data packet sequence of low-frequency traffic and evaluate the logical coherence level of the traffic packets;

[0011] Determine the priority scheduling strategy for low-frequency traffic based on the interference intensity of links at different levels and the logical coherence level of traffic packets;

[0012] In the network switch, resources are allocated and scheduled for compressed high-frequency traffic and prioritized low-frequency traffic.

[0013] In a preferred embodiment, all network traffic entering the network switch is monitored, the frequency characteristics of each traffic are obtained, and the network traffic is classified into high-frequency traffic and low-frequency traffic according to the frequency characteristics of the traffic, specifically including:

[0014] Collect all traffic data entering the network switch in real time and record the basic characteristic information of each traffic;

[0015] Extract frequency features of collected flow data based on the preset frequency analysis model and output the frequency feature value of each flow;

[0016] The frequency characteristic value is input into the classification rule module, and the traffic is divided into high-frequency traffic and low-frequency traffic categories according to the classification standard of the frequency range, and corresponding identification information is added to the high-frequency traffic and low-frequency traffic.

[0017] In a preferred embodiment, high-frequency traffic is compressed using a dynamic traffic compression algorithm, specifically including:

[0018] Read data classified as high-frequency traffic and determine the real-time bandwidth usage of high-frequency traffic;

[0019] Dynamically evaluate the real-time bandwidth usage of high-frequency traffic and generate compression parameters;

[0020] A dynamic traffic compression algorithm is applied to high-frequency traffic data for compression based on compression parameters.

[0021] In a preferred embodiment, the topological links of low-frequency traffic are analyzed by sparse representation iterative reweighted least squares method to identify the link interference strength at different levels, specifically including:

[0022] Extract the traffic data marked as low-frequency traffic and associate it with the corresponding topological link information;

[0023] Construct a sparse interference matrix containing link bandwidth utilization and traffic load distribution as analysis input;

[0024] The sparse interference matrix is ​​solved using iterative reweighted least squares method to identify significant interference patterns.

[0025] The interference strength of links at different levels is calculated based on the significant interference patterns, and a link interference strength matrix is ​​generated.

[0026] In a preferred embodiment, the sparse interference matrix is ​​solved using iterative reweighted least squares to identify significant interference patterns, specifically:

[0027] The iterative reweighted least squares method is applied to the sparse interference matrix to solve the interference weight of the link through the optimization function. The optimization objective function is: ;in, , represents the interference weight set of the link; For the Interference weight of the link; is the number of links; is the amount of low-frequency traffic; Indicates the The low-frequency flow is Bandwidth ratio on the links;

[0028] The solution process of iterative reweighted least squares method includes:

[0029] Initialize the interference weight set ; Calculate the weighted error term and update Value: ;in, Indicates the The link in The weight value obtained after iteration is updated; is a regularization parameter that prevents the weight from going to zero; Indicates the number of iterations;

[0030] Repeat the iteration until the weight change meets the convergence condition.

[0031] In a preferred embodiment, a variational autoencoder is used to analyze a sequence of low-frequency traffic packets to evaluate the logical coherence level of the traffic packets, specifically including:

[0032] Extract the data packet sequence of low-frequency traffic, rearrange it in chronological order and construct a sequence sample set;

[0033] Perform feature encoding on the sequence sample set and convert the attributes of each data packet into a high-dimensional vector representation;

[0034] Use the variational autoencoder model to reduce the dimension of the feature vector and generate the potential feature representation of the sequence;

[0035] Based on the latent feature representation, a logical coherence score is calculated to quantify the logical coherence level of the traffic packet.

[0036] In a preferred embodiment, based on the potential feature representation, a logical coherence score is calculated to quantify the logical coherence level of the traffic packet, specifically:

[0037] Latent feature representation generated by variational autoencoder , quantify the logical coherence of each time window: ;in, represents the logical coherence score, It is The latent feature representation of the time window is represents the feature difference between adjacent windows, Indicates the total number of time windows, Indicates the number of the time window.

[0038] In a preferred embodiment, a priority scheduling strategy for low-frequency traffic is determined based on the interference intensity of links at different levels and the logical coherence level of traffic packets, specifically including:

[0039] Read the link interference intensity matrix and logical coherence score, and calculate the resource allocation priority of low-frequency traffic on the link based on the link interference intensity matrix;

[0040] Based on the logical coherence score, the priority weight of low-frequency traffic is adjusted to generate a comprehensive priority score;

[0041] Based on the comprehensive priority score sorting, a priority scheduling strategy for low-frequency traffic is generated.

[0042] In a preferred embodiment, in a network switch, resource allocation and scheduling of compressed high-frequency traffic and prioritized low-frequency traffic specifically include:

[0043] Based on the compressed high-frequency traffic and priority scheduling policy, the resource requirements of each traffic are extracted from the scheduling table;

[0044] Calculate the resource allocation ratio between high-frequency traffic and low-frequency traffic based on the current available resource status of the network switch;

[0045] Prioritize resources required for low-frequency traffic according to the allocation ratio, and allocate the remaining resources to high-frequency traffic;

[0046] Update resource allocation tables and perform scheduling operations in real time in network switches.

[0047] In another aspect, the present invention provides a network switch control system, including a traffic monitoring and classification module, a high-frequency traffic compression module, a link interference analysis module, a logical coherence assessment module, a priority strategy determination module, and a resource allocation and scheduling module;

[0048] Traffic monitoring and classification module: monitors all network traffic entering the network switch, obtains the frequency characteristics of each flow, and classifies the network traffic into high-frequency traffic and low-frequency traffic based on the frequency characteristics of the traffic;

[0049] High-frequency traffic compression module: applies dynamic traffic compression algorithm to high-frequency traffic for compression processing;

[0050] Link Interference Analysis Module: This module analyzes the topological links of low-frequency traffic using the sparse representation iterative reweighted least squares method to identify link interference intensity at different levels.

[0051] Logical coherence assessment module: This module analyzes low-frequency traffic data packet sequences using a variational autoencoder to assess the logical coherence of traffic packets.

[0052] Priority strategy determination module: Determines the priority scheduling strategy for low-frequency traffic based on the interference intensity of links at different levels and the logical coherence level of traffic packets;

[0053] Resource allocation and scheduling module: In the network switch, resources are allocated and scheduled for compressed high-frequency traffic and prioritized low-frequency traffic.

[0054] The technical effects and advantages of the network switch control method and system of the present invention are as follows:

[0055] 1. The present invention classifies network traffic and divides the traffic entering the network switch into high-frequency traffic and low-frequency traffic according to frequency characteristics, thereby achieving targeted processing. The dynamic traffic compression algorithm is applied to high-frequency traffic, which can not only effectively reduce the occupation of network bandwidth by high-frequency traffic, but also avoid the conflict problem of low-frequency traffic in network resource allocation by optimizing the compression strategy. The topological link of low-frequency traffic is analyzed by sparse representation iterative reweighted least squares method, which can accurately identify the interference intensity of different levels in the link, and provide an accurate link status basis for subsequent priority scheduling of low-frequency traffic, thereby improving the transmission stability and reliability of low-frequency traffic.

[0056] 2. By using a variational autoencoder to perform logical coherence analysis on the packet sequence of low-frequency traffic, the present invention further quantifies the transmission characteristics of low-frequency traffic, enabling the priority scheduling strategy to be dynamically optimized in combination with link interference and packet logical characteristics. In the network switch, through real-time resource allocation and scheduling mechanisms, the resource requirements of low-frequency traffic are prioritized, while the compressed high-frequency traffic resources are reasonably allocated to ensure the coordinated transmission of high-frequency and low-frequency traffic, solving the problem of high-frequency traffic compression interfering with low-frequency traffic, and improving the utilization efficiency of network resources and the real-time transmission performance of low-frequency traffic. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of a network switch control method according to the present invention;

[0058] Figure 2 The figure is a schematic structural diagram of a network switch control system of the present invention. DETAILED DESCRIPTION

[0059] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] Example 1: Figure 1 The present invention provides a network switch control method, which includes the following steps:

[0061] Monitor all network traffic entering the network switch, obtain the frequency characteristics of each flow, and classify the network traffic into high-frequency traffic and low-frequency traffic based on the frequency characteristics of the traffic.

[0062] Apply dynamic traffic compression algorithm to compress high-frequency traffic.

[0063] The topological links of low-frequency traffic are analyzed by sparse representation iterative reweighted least squares method to identify the link interference intensity at different levels.

[0064] The data packet sequence of low-frequency traffic is analyzed by variational autoencoder to evaluate the logical coherence level of the traffic packets.

[0065] Based on the interference intensity of links at different levels and the logical continuity level of traffic packets, the priority scheduling strategy for low-frequency traffic is determined.

[0066] In the network switch, resources are allocated and scheduled for compressed high-frequency traffic and prioritized low-frequency traffic.

[0067] Monitor all network traffic entering the network switch, obtain the frequency characteristics of each flow, and classify network traffic into high-frequency traffic and low-frequency traffic based on the frequency characteristics of the traffic, including:

[0068] Collect all traffic data entering the network switch in real time and record the basic characteristic information of each traffic:

[0069] By capturing all data packets on the switch port in real time, basic feature information such as the source address, destination address, packet size, and protocol type of the traffic is recorded in sequence; each data packet is sorted by timestamp to ensure that the collected traffic data is continuous in the time dimension; the collected data is stored in the buffer area as the basis for subsequent frequency feature extraction.

[0070] The frequency characteristics of the collected flow data are extracted based on the preset frequency analysis model, and the frequency characteristic value of each flow is output:

[0071] By reading the data in the buffer area, the data is grouped according to the source address and destination address of the traffic to generate their own time series; the frequency analysis method of short-time Fourier transform is used to process each time series, and the average value of the main frequency components is calculated as the frequency characteristic value of the corresponding traffic. All extracted frequency characteristic values ​​are stored in the feature database, and unique identification information is attached to correspond to the original traffic data.

[0072] The frequency characteristic value is input into the classification rule module. Based on the classification standard of the frequency range, the traffic is divided into high-frequency traffic and low-frequency traffic categories, and corresponding identification information is added to the high-frequency traffic and low-frequency traffic:

[0073] Read the frequency feature values ​​in the feature database and perform classification operations according to the set classification rules (such as the frequency of high-frequency traffic is greater than a certain threshold, and the frequency of low-frequency traffic is lower than the threshold); the classification result is associated with the unique identifier of the original traffic, and the classification result and classification basis information are marked. At the same time, a category label is added to the classified traffic for subsequent processing.

[0074] Apply dynamic traffic compression algorithms to high-frequency traffic for compression processing, including:

[0075] Read data classified as high-frequency traffic to determine the real-time bandwidth usage of high-frequency traffic:

[0076] Traffic data marked as high-frequency traffic is extracted from the classification results, and the bandwidth monitoring tool is called to perform real-time analysis on the extracted traffic to calculate the average bandwidth occupancy and peak bandwidth occupancy of each flow. By combining the size of the data packet with its transmission time, the instantaneous transmission rate and bandwidth usage pattern of each flow are obtained. At the same time, the historical transmission records of high-frequency traffic are statistically analyzed to determine the long-term trend of traffic bandwidth occupancy. All calculation results are stored in a temporary storage area to provide input for the next dynamic evaluation.

[0077] Dynamically evaluate the real-time bandwidth usage of high-frequency traffic and generate compression parameters:

[0078] Based on the stored bandwidth usage, a real-time optimization algorithm is used to dynamically evaluate bandwidth usage patterns. The compression ratio and redundant data discard threshold suitable for the current network status are determined by calculating the network load factor and traffic burst index. The compression ratio is determined by the proportion of bandwidth occupied and the remaining amount of available network resources. The redundant data discard threshold is dynamically adjusted by detecting the packet repetition rate. All compression parameters are stored with the unique traffic identifier as the index, so that they can be directly called in subsequent compression processing.

[0079] Apply dynamic traffic compression algorithm to compress high-frequency traffic data based on compression parameters:

[0080] The dynamically generated compression parameters are read and the dynamic traffic compression algorithm is called to process high-frequency traffic. The compression algorithm uses redundant data deduplication technology based on content identification. First, the content characteristics of the traffic data packets are analyzed to detect and remove duplicate content. Secondly, the predictive coding method is applied to encode the content that may be reused into short tags to further reduce the amount of data. The processed data packets maintain the temporal order of the original traffic and append compression tag information to ensure the compatibility of subsequent operations.

[0081] The sparse representation iterative reweighted least squares method is used to analyze the topological links of low-frequency traffic and identify the link interference intensity at different levels, including:

[0082] Extract the traffic data marked as low-frequency traffic and associate it with the corresponding topological link information:

[0083] Data marked as low-frequency traffic is extracted from the classification results. The extracted low-frequency traffic data includes traffic identifiers, source addresses, destination addresses, and time series information. Furthermore, based on the topological structure, the transmission paths corresponding to the low-frequency traffic are associated with topological links. This associated information includes the link numbers that the low-frequency traffic passes through, the physical connection relationships between the links, and the transmission direction. This associated information is stored as a topological link data structure, and the real-time status of each link is used as the basic input for the subsequent sparse interference matrix.

[0084] Construct a sparse interference matrix containing link bandwidth utilization and traffic load distribution as analysis input:

[0085] Based on the extracted low-frequency traffic and topology link information, a sparse interference matrix is ​​constructed. Assuming that the network topology contains Links and low-frequency traffic, construct a sparse interference matrix of size M×N, and each element in the sparse interference matrix is ​​expressed as: ;in, Indicates the The low-frequency flow is The bandwidth ratio on the links is calculated by the traffic demand and the available bandwidth of the links; Indicates the The bandwidth requirement of low-frequency traffic in bits per second; Indicates the The low-frequency traffic flows through the The probability of a link; Indicates the The total available bandwidth of the link, in bits per second.

[0086] The sparse interference matrix only retains the elements with higher bandwidth utilization and sets the remaining elements to zero to ensure the sparsity of the matrix. The constructed matrix is ​​stored in sparse matrix form to provide input for subsequent iterative solutions.

[0087] The sparse interference matrix is ​​solved using iterative reweighted least squares to identify significant interference patterns:

[0088] The iterative reweighted least squares (IRLS) method is applied to the sparse interference matrix to identify significant interference patterns in topological links. The goal of IRLS is to solve the interference weight of the link by optimizing the function. The optimization objective function is: ;in, , represents the interference weight set of the link; For the The interference weight of the link, the initial value is 1; is the number of links; is the amount of low-frequency traffic.

[0089] IRLS solution process:

[0090] Initialize the interference weight set .

[0091] Calculate the weighted error term and update Value: ;in, Indicates the The link in The weight value obtained after iteration is updated; is a regularization parameter that prevents the weight from going to zero; Indicates the number of iterations, used to track each round of the IRLS algorithm.

[0092] Repeat the iteration until the weight change meets the convergence condition.

[0093] The final result Indicates the interference weight of the link. The larger the interference weight, the more significant the interference pattern of the link.

[0094] Among them, generally speaking, It is an empirical value, and its size is determined by the numerical range and sparsity of the actual problem. If the numerical range of the sparse interference matrix is ​​large, a smaller value can be selected. ; If the sparse interference matrix is ​​sparse, a larger To stabilize the calculation.

[0095] Among them, the link with less interference occupies a larger value in the final weight, which means that the link has less impact on the scheduling of low-frequency traffic; the link with significant interference occupies a smaller value in the final weight, indicating that the link needs to be given priority to avoid its adverse impact on traffic.

[0096] The interference strength of links at different levels is calculated based on the significant interference pattern, and the link interference strength matrix is ​​generated:

[0097] Using the obtained interference weight set, the links at different levels are grouped and the interference strength is calculated. The link interference strength is defined as: ;in, Indicates the The link interference strength at each level, Indicates the The set of links contained in each level, For the The interference weight of the link, Indicates the level number to which a link belongs in the network topology.

[0098] The calculation results of the link interference strength are stored as a link interference strength matrix, where each element corresponds to the interference strength of a layer.

[0099] By analyzing the topological links of low-frequency traffic through sparse representation iterative reweighted least squares method, the interference intensity of links at different levels can be efficiently identified. First, by constructing a sparse interference matrix, the bandwidth usage characteristics of low-frequency traffic in complex network topologies can be accurately reflected, while avoiding processing non-critical link data, thereby reducing computational complexity; second, the iterative reweighted least squares method can dynamically adjust weights and adaptively optimize significant interference patterns to improve the accuracy of the analysis results; finally, by calculating the hierarchical interference intensity of the link, a clear quantitative basis is provided for subsequent traffic priority scheduling, making resource allocation more targeted and scientific. Overall, this step takes into account both efficiency and accuracy of analysis and is suitable for dynamic traffic management in large-scale network environments.

[0100] The variational autoencoder is used to analyze the low-frequency traffic data packet sequence and evaluate the logical coherence level of the traffic packets, including:

[0101] Extract the packet sequence of low-frequency traffic, rearrange it in chronological order and construct a sequence sample set:

[0102] The data packet sequences marked as low-frequency traffic are extracted from the classification results. The extracted attributes include source address, destination address, timestamp, protocol type, and packet size. The extracted data packets are rearranged according to timestamps to generate a time-continuous sequence dataset.

[0103] To ensure the integrity of the samples, the time series is divided into fixed-length windows, each containing a certain number of data packets, and windows of insufficient length are padded with null values. The resulting sequence sample set is stored in a structured form to provide input for subsequent steps.

[0104] Perform feature encoding on the sequence sample set and convert the attributes of each data packet into a high-dimensional vector representation:

[0105] Perform feature encoding on the sequence sample set generated in step 1. Assume that the attribute set of the data packet includes source address, destination address, timestamp, protocol type and packet size. Encode these attributes independently:

[0106] Source address and destination address: encoded as a fixed-dimensional vector through hash mapping;

[0107] Timestamp: Convert to a standard time interval and map to the [0,1][0,1] interval using a normalization method;

[0108] Protocol type: One-hot encoding is used to map discrete values ​​into vectors;

[0109] Packet size: Logarithmically normalized to enhance the smoothness of the value range.

[0110] Assume that the feature vector of the data packet is represented as , then the sequence samples of the entire time window can be expressed as the feature matrix ;in, Indicates the total number of packets contained in the time window, Represents the index of a single data packet in the time window in the sequence sample, It is a d-dimensional vector, where d is the feature dimension after encoding.

[0111] The variational autoencoder model is used to reduce the dimension of the feature vector and generate the potential feature representation of the sequence:

[0112] For the feature matrix Input the variational autoencoder model for dimensionality reduction. The core of the variational autoencoder consists of an encoder and a decoder, and its goal is to learn the potential feature representation .

[0113] The encoder transforms the Mapped to distribution parameters in the latent space: , ;in, is the mean of the underlying distribution, is the standard deviation of the underlying distribution, and It is a trainable neural network.

[0114] in, Represents the encoder neural network used to calculate the potential distribution mean in the variational autoencoder, which maps the input high-dimensional feature matrix through multiple layers of nonlinear transformations to ; represents the encoder neural network used to calculate the standard deviation of the latent distribution in the variational autoencoder, which is similar to , but its goal is to output ; Represents the central position in the potential feature distribution, which is the encoder network The output of is used to describe the principal component positions of the potential distribution in the latent space; Represents the extended range of the potential feature distribution, which is the encoder network The output of is used to describe the degree of variation of features in the latent space. The larger the standard deviation, the more dispersed the distribution.

[0115] Use the reparameterization trick to sample from a Gaussian distribution: ;in, is the potential feature representation after dimensionality reduction, is the noise sampled from a standard normal distribution, represents the standard normal distribution.

[0116] The decoder starts from the latent representation Reconstructing input features : ;in, is the reconstructed feature matrix, is the decoder network.

[0117] in, Represents the decoder neural network in the variational autoencoder. It uses multiple layers of nonlinear mapping to represent the potential features. Restore input features to high-dimensional space ,The goal of the decoder is to reconstruct the input features while minimizing the error with the original input.

[0118] The training goal is to minimize the reconstruction error and the KL divergence of the latent distribution: ;in, represents the total loss function of the variational autoencoder; Represents the potential distribution With prior distribution The KL divergence of is used to constrain the distribution shape of the latent space to make it close to the standard normal distribution.

[0119] Based on the latent feature representation, a logical coherence score is calculated to quantify the logical coherence level of the traffic packet:

[0120] Latent feature representation generated by variational autoencoder , quantify the logical coherence of each time window. The specific calculation formula is as follows: ;in, represents the logical coherence score, It is The latent feature representation of the time window is represents the feature difference between adjacent windows, Indicates the total number of time windows, Indicates the number of the time window.

[0121] The lower the logical coherence score, the higher the logical coherence of the traffic packet; the higher the logical coherence score, the worse the logical coherence; the final result is stored as a logical coherence score matrix for subsequent priority scheduling strategy.

[0122] Based on the interference intensity of different layers of links and the logical coherence level of traffic packets, a priority scheduling strategy for low-frequency traffic is determined, including:

[0123] Read the link interference intensity matrix and logical coherence score, and calculate the resource allocation priority of low-frequency traffic on the link based on the link interference intensity matrix:

[0124] The link interference intensity associated with low-frequency traffic is extracted from the link interference intensity matrix. The link interference intensity of each link indicates the congestion or stability of the link. Then, for each low-frequency traffic flow, the link interference intensity of all links along the path is analyzed. Based on the distribution of traffic flow on each link, its overall resource allocation priority is calculated. The calculation method is to comprehensively consider the traffic occupancy ratio on each link and the cumulative result of the corresponding link interference intensity. The final result is stored in the form of a priority allocation table for subsequent analysis.

[0125] Based on the logical coherence score, the priority weight of low-frequency traffic is adjusted to generate a comprehensive priority score:

[0126] The logical coherence score of each low-frequency flow is read. The logical coherence score reflects the integrity of the flow packet sequence during transmission. The logical coherence score is used as a weighting factor to adjust the impact of link resource allocation priority. The specific adjustment method is: for flows with poor logical coherence, a higher adjustment factor weight is assigned to reduce their overall priority; for flows with good logical coherence, their original impact value of link allocation priority is retained. The adjusted comprehensive priority score is recorded in the priority score table and serves as the basis for generating scheduling policies.

[0127] The comprehensive priority score is the adjusted result calculated by combining the link resource priority and the logical coherence score. The specific calculation logic is as follows:

[0128] The lower the logical coherence score, the greater the impact on the link resource priority; the higher the logical coherence score, the lower the weight of the link priority.

[0129] The calculation of the comprehensive priority score can be described as follows: the link priority assignment value is weighted and adjusted according to the proportion of the logical coherence score. The adjusted value is the comprehensive priority score. The adjustment rule is determined by the logical coherence score and a balance factor to ensure that the influence of link priority and logical coherence score in the overall scheduling is properly balanced.

[0130] Based on the comprehensive priority score, a priority scheduling policy for low-frequency traffic is generated:

[0131] Low-frequency traffic is ranked by its comprehensive priority score and a priority scheduling table is generated, sorting the scores from high to low. The scheduling table contains the priority level, corresponding link path, and scheduling rules for each low-frequency traffic flow. High-priority traffic is allocated more network resources, and scheduling rules ensure that it is processed before low-priority traffic. The priority scheduling table can be used by network switches for real-time scheduling.

[0132] In the network switch, resources are allocated and scheduled for compressed high-frequency traffic and prioritized low-frequency traffic, specifically including:

[0133] Based on the compressed high-frequency traffic and priority scheduling policy, the resource requirements of each traffic are extracted from the scheduling table:

[0134] Read the compressed high-frequency and low-frequency traffic priority scheduling table information. The scheduling table includes the bandwidth requirements, delay requirements, priority level, and corresponding link path data for each flow.

[0135] The resource requirements of each flow are analyzed to extract its actual bandwidth requirement value and the minimum guaranteed bandwidth threshold. This information provides a basis for resource allocation and ensures that the requirements of high-frequency and low-frequency flows can be accurately identified.

[0136] Combined with the current available resource status of the network switch, calculate the resource allocation ratio of high-frequency traffic to low-frequency traffic:

[0137] By querying the resource usage of the current link, including remaining bandwidth, link load, and available time slices, the resource allocation ratio is calculated based on the resource requirements of high-frequency and low-frequency traffic. The resource allocation ratio prioritizes the needs of low-frequency traffic and dynamically adjusts the allocation strategy of remaining resources based on the compressed bandwidth requirements of high-frequency traffic. The allocation ratio comprehensively considers traffic priority, link load, and fairness of bandwidth allocation. The results are recorded in an allocation ratio table.

[0138] Prioritize resources required for low-frequency traffic according to the allocation ratio, and allocate the remaining resources to high-frequency traffic:

[0139] Based on the allocation ratio table, resources are first allocated to low-frequency traffic to ensure that its key requirements are met, such as the minimum guaranteed bandwidth and priority of the link. Subsequently, the remaining link resources are allocated to high-frequency traffic, leveraging its compressed traffic characteristics to complete transmission while minimizing resource usage.

[0140] During the allocation process, the allocation status of each link is monitored in real time to prevent resource allocation from exceeding the available capacity of the link.

[0141] Update resource allocation tables and perform scheduling operations in real time on network switches:

[0142] The allocated resource information is recorded in the resource allocation table, including the allocated bandwidth, priority level and corresponding link resource usage of each flow; the real-time updated allocation table is used to guide the execution of scheduling rules of the network switch.

[0143] During the scheduling process, link status changes are monitored. If resources are insufficient or the load exceeds the limit, reallocation operations are triggered to ensure the continuity and stability of resource allocation and scheduling.

[0144] Example 2: The difference between Example 2 of the present invention and Example 1 is that this example introduces a network switch control system.

[0145] Figure 2 A structural schematic diagram of a network switch control system of the present invention is given, which includes a traffic monitoring and classification module, a high-frequency traffic compression module, a link interference analysis module, a logical coherence evaluation module, a priority strategy determination module, and a resource allocation and scheduling module.

[0146] Traffic monitoring and classification module: monitors all network traffic entering the network switch, obtains the frequency characteristics of each flow, and classifies the network traffic into high-frequency traffic and low-frequency traffic based on the frequency characteristics of the traffic.

[0147] High-frequency traffic compression module: applies dynamic traffic compression algorithm to high-frequency traffic for compression processing.

[0148] Link Interference Analysis Module: Analyzes the topological links of low-frequency traffic through sparse representation iterative reweighted least squares method to identify link interference intensity at different levels.

[0149] Logical coherence evaluation module: Analyzes the data packet sequence of low-frequency traffic through a variational autoencoder to evaluate the logical coherence level of the traffic packets.

[0150] Priority strategy determination module: Determines the priority scheduling strategy for low-frequency traffic based on the interference intensity of links at different levels and the logical continuity level of traffic packets.

[0151] Resource allocation and scheduling module: In the network switch, resources are allocated and scheduled for compressed high-frequency traffic and prioritized low-frequency traffic.

[0152] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0153] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0154] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0155] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0156] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0157] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0158] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0159] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0160] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0161] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A network switch control method, characterized in that: The steps include: Monitor all network traffic entering the network switch, obtain the frequency characteristics of each flow, and classify the network traffic into high-frequency traffic and low-frequency traffic based on the frequency characteristics of the traffic; Apply dynamic traffic compression algorithm to compress high-frequency traffic; The sparse representation iterative reweighted least squares method is used to analyze the topological links of low-frequency traffic and identify the link interference intensity at different levels, including: Extract the traffic data marked as low-frequency traffic and associate it with the corresponding topological link information; Construct a sparse interference matrix containing link bandwidth utilization and traffic load distribution as analysis input; The sparse interference matrix is ​​solved using iterative reweighted least squares method to identify significant interference patterns. Calculate the interference strength of links at different levels based on the significant interference patterns and generate a link interference strength matrix; The variational autoencoder is used to analyze the data packet sequence of low-frequency traffic and evaluate the logical coherence level of the traffic packets, including: Extract the data packet sequence of low-frequency traffic, rearrange it in chronological order and construct a sequence sample set; Perform feature encoding on the sequence sample set and convert the attributes of each data packet into a high-dimensional vector representation; Use the variational autoencoder model to reduce the dimension of the feature vector and generate the potential feature representation of the sequence; Based on the latent feature representation, a logical coherence score is calculated to quantify the logical coherence level of the traffic packet; Based on the interference intensity of links at different levels and the logical coherence level of traffic packets, a priority scheduling strategy for low-frequency traffic is determined, including: Read the link interference intensity matrix and logical coherence score, and calculate the resource allocation priority of low-frequency traffic on the link based on the link interference intensity matrix; Based on the logical coherence score, the priority weight of low-frequency traffic is adjusted to generate a comprehensive priority score; Generate a priority scheduling strategy for low-frequency traffic based on the comprehensive priority score ranking; In the network switch, resources are allocated and scheduled for compressed high-frequency traffic and prioritized low-frequency traffic.

2. A network switch control method according to claim 1, characterized in that: Monitor all network traffic entering the network switch, obtain the frequency characteristics of each flow, and classify network traffic into high-frequency traffic and low-frequency traffic based on the frequency characteristics of the traffic, including: Collect all traffic data entering the network switch in real time and record the basic characteristic information of each traffic; Extract frequency features of collected flow data based on the preset frequency analysis model and output the frequency feature value of each flow; The frequency characteristic value is input into the classification rule module, and the traffic is divided into high-frequency traffic and low-frequency traffic categories according to the classification standard of the frequency range, and corresponding identification information is added to the high-frequency traffic and low-frequency traffic.

3. A network switch control method according to claim 2, characterized in that: Apply dynamic traffic compression algorithms to high-frequency traffic for compression processing, including: Read data classified as high-frequency traffic and determine the real-time bandwidth usage of high-frequency traffic; Dynamically evaluate the real-time bandwidth usage of high-frequency traffic and generate compression parameters; A dynamic traffic compression algorithm is applied to high-frequency traffic data for compression based on compression parameters.

4. A network switch control method according to claim 3, characterized in that: The sparse interference matrix is ​​solved using iterative reweighted least squares to identify significant interference patterns, specifically: The iterative reweighted least squares method is applied to the sparse interference matrix to solve the interference weight of the link through the optimization function. The optimization objective function is: Where W={W1,W2,...,W M }, represents the interference weight set of the link; W i is the interference weight of the i-th link; M is the number of links; N is the number of low-frequency traffic; R i，j represents the bandwidth ratio of the j-th low-frequency traffic on the i-th link; The solution process of iterative reweighted least squares method includes: Initialize the interference weight set W; calculate the weighted error term and update the value of W: in, represents the updated weight value of the i-th link after the k+1-th iteration; ∈ is the regularization parameter to prevent the weight from tending to zero; k represents the number of iterations; Repeat the iteration until the weight change meets the convergence condition.

5. A network switch control method according to claim 4, characterized in that: Based on the latent feature representation, a logical coherence score is calculated to quantify the logical coherence level of the traffic packet, specifically: The latent feature representation Z generated by the variational autoencoder quantifies the logical coherence of each time window: Among them, S logic Indicates the logical coherence score, Z u is the potential feature representation of the u-th time window, ||Z u -Z u-1 || 2 Indicates the feature difference between adjacent windows, U indicates the total number of time windows, and u indicates the number of the time window.

6. A network switch control method according to claim 5, characterized in that: In the network switch, resources are allocated and scheduled for compressed high-frequency traffic and prioritized low-frequency traffic, specifically including: Based on the compressed high-frequency traffic and priority scheduling policy, the resource requirements of each traffic are extracted from the scheduling table; Calculate the resource allocation ratio between high-frequency traffic and low-frequency traffic based on the current available resource status of the network switch; Prioritize resources required for low-frequency traffic according to the allocation ratio, and allocate the remaining resources to high-frequency traffic; Update resource allocation tables and perform scheduling operations in real time in network switches.

7. A network switch control system, used to implement a network switch control method according to any one of claims 1 to 6, characterized in that: It includes traffic monitoring and classification module, high-frequency traffic compression module, link interference analysis module, logical coherence evaluation module, priority strategy determination module and resource allocation and scheduling module; Traffic monitoring and classification module: monitors all network traffic entering the network switch, obtains the frequency characteristics of each flow, and classifies the network traffic into high-frequency traffic and low-frequency traffic based on the frequency characteristics of the traffic; High-frequency traffic compression module: applies dynamic traffic compression algorithm to high-frequency traffic for compression processing; Link Interference Analysis Module: This module analyzes the topological links of low-frequency traffic using the sparse representation iterative reweighted least squares method to identify link interference intensity at different levels. Logical coherence assessment module: This module analyzes low-frequency traffic data packet sequences using a variational autoencoder to assess the logical coherence of traffic packets. Priority strategy determination module: Determines the priority scheduling strategy for low-frequency traffic based on the interference intensity of links at different levels and the logical coherence level of traffic packets; Resource allocation and scheduling module: In the network switch, resources are allocated and scheduled for compressed high-frequency traffic and prioritized low-frequency traffic.

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