Weak network adaptive algorithm system based on dynamic network monitoring

Abstract

The invention belongs to the technical field of network self-adaption, and particularly relates to a weak network self-adaption algorithm system based on dynamic network monitoring, which comprises the following steps of: deploying a monitoring agent on a network node; extracting network bandwidth, delay and packet loss rate from the network state report, preprocessing the extracted data, and extracting feature vectors from the preprocessed data; adjusting the size of a buffer area for data transmission according to the network bandwidth and delay; and controlling the sending and receiving of data according to the adjusted algorithm and parameter, monitoring the data transmission state in real time in the data transmission process, feeding back the data transmission state information, and adjusting the algorithm and parameter in time according to the feedback information. The problem of how to improve the efficiency and reliability of data transmission under the conditions of complex and changeable network environment, low bandwidth, high delay, serious packet loss and the like in the prior art is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of network adaptation, and in particular relates to a weak network adaptation algorithm system based on dynamic network monitoring. Background Art

[0002] In modern network communications, the complexity and uncertainty of the network environment are increasing. The emergence of weak network environment has brought huge challenges to data transmission. At present, some solutions have been proposed for the data transmission problem in weak network environment, mainly including the following: TCP protocol optimization; UDP protocol improvement; lossless compression; lossy compression; fountain code; low-density parity check code.

[0003] Although the existing technology has solved the problem of data transmission in weak network environments to a certain extent, it still has the following shortcomings: lack of dynamic adaptability. Existing algorithms are usually designed based on fixed network parameters and models, and cannot perceive changes in the network environment in real time. As a result, when network conditions change, the algorithm cannot be adjusted in time, thus affecting the performance of data transmission; lack of targeting for weak network environments. Weak network environments have their own unique characteristics, such as high latency, high packet loss rate, bandwidth fluctuations, etc. Existing algorithms often do not fully consider these characteristics when designing, resulting in poor performance in weak network environments; the system is highly complex. Some complex algorithms and protocols require high costs in implementation and maintenance, and may introduce new problems and failures.

[0004] Therefore, traditional network algorithms often cannot effectively adapt to changes in weak network environments, resulting in decreased data transmission performance, such as increased transmission delay, increased packet loss rate, low bandwidth utilization, etc. Therefore, a system is needed that can monitor the network status in real time and adaptively adjust the algorithm according to network conditions to improve the performance and reliability of data transmission. Summary of the invention

[0005] In order to solve the problems in the prior art, the present invention provides a weak network adaptive algorithm system based on dynamic network monitoring, which solves the problem of how to improve the efficiency and reliability of data transmission under the conditions of complex and changeable network environment, low bandwidth, high latency and severe packet loss.

[0006] To achieve the above purpose, the specific technical solution is as follows:

[0007] One of the present inventions is to provide a weak network self-adaptation method based on dynamic network monitoring, comprising the following steps:

[0008] By deploying monitoring agents on network nodes, the size, sending time, and receiving time of network data packets are collected, and the collected data is analyzed to calculate the network bandwidth, delay, and packet loss rate, and a status report is generated with the analyzed network status information; the network bandwidth, delay, and packet loss rate are extracted from the network status report, and the extracted data are preprocessed, and feature vectors are extracted from the preprocessed data; the data transmission buffer size is adjusted according to the network bandwidth and delay, the data transmission frequency is adjusted according to the network load and delay, and the data retransmission strategy is adjusted according to the packet loss rate and network delay; the sending and receiving of data is controlled according to the adjusted algorithm and parameters, and during the data transmission process, the status of data transmission is monitored in real time, the status information of data transmission is fed back, and the algorithm and parameters are adjusted in time according to the feedback information.

[0009] The second aspect of the present invention is to provide a weak network adaptive algorithm system based on dynamic network monitoring, comprising:

[0010] Network monitoring module: responsible for real-time monitoring of network status, including parameters: network bandwidth, delay, and packet loss rate;

[0011] Data processing module: processes the received network status reports, extracts key information, and performs data preprocessing according to preset algorithms and rules;

[0012] Algorithm adjustment module: According to the feature vector extracted by the data processing module, combined with the preset algorithm and rules, adjust the algorithm and parameters of data transmission to adapt to changes in the network environment;

[0013] Transmission control module: controls the data transmission process according to the algorithm and parameters adjusted by the algorithm adjustment module to ensure accurate and reliable data transmission.

[0014] The third aspect of the present invention is to provide a weak network adaptive device based on dynamic network monitoring, and the weak network adaptive device based on dynamic network monitoring is communicatively connected to the weak network adaptive algorithm system based on dynamic network monitoring described in claim 7.

[0015] A fourth aspect of the present invention is to provide a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements a weak network self-adaptation method based on dynamic network monitoring.

[0016] Compared with the prior art, the beneficial effects of the present invention are at least:

[0017] The present invention can perceive the changes in the network environment in real time. When the network conditions change, the algorithm can be adjusted in time, thereby improving the performance of data transmission;

[0018] The present invention can take into account the unique characteristics in a weak network environment, including high latency, high packet loss rate, bandwidth fluctuation, etc., and improve the performance of network monitoring in a weak network environment;

[0019] The algorithms and protocols selected in the present invention have low costs in implementation and maintenance, and reduce the generation of problems and failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the system framework of the present invention;

[0021] Figure 2 It is a schematic diagram of the working process of the system of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be described in detail below in conjunction with the specific embodiments shown in the accompanying drawings. However, these embodiments do not limit the present invention, and any structural, methodological or functional changes made by a person skilled in the art based on these embodiments are all within the scope of protection of the present invention.

[0023] Embodiment 1:

[0024] like Figure 1 As shown, the present invention aims to provide a weak network adaptive algorithm system based on dynamic network monitoring to solve the problems in the prior art. The system architecture is:

[0025] S1: Network monitoring module

[0026] Function: responsible for real-time monitoring of network status, including network bandwidth, delay, and packet loss rate;

[0027] Implementation steps:

[0028] S101: Data collection: by deploying monitoring agents on network nodes, collect relevant information of network data packets, including the size, sending time, and receiving time of the data packets;

[0029] S102: Data analysis: Analyze the collected data and calculate the parameters of network bandwidth, delay, and packet loss rate;

[0030] S103: Status report: Generate a status report based on the analyzed network status information and send it to the data processing module.

[0031] S2: Data processing module

[0032] Function: Process the received network status report, extract key information, and perform data preprocessing according to preset algorithms and rules;

[0033] Implementation steps:

[0034] S201: Information extraction: extracting key information of network bandwidth, delay, and packet loss rate from the network status report;

[0035] S202: Data preprocessing

[0036] Data cleaning: Clean the extracted data to remove noise and abnormal data;

[0037] Data conversion: convert the cleaned data to meet the input requirements of algorithms and models;

[0038] S103: Feature extraction: Extract feature vectors from the preprocessed data for subsequent algorithm adjustment and model training.

[0039] S3: Algorithm tuning module

[0040] Function: According to the feature vector extracted by the data processing module, combined with the preset algorithms and rules, adjust the data transmission algorithm and parameters to adapt to changes in the network environment;

[0041] Implementation steps:

[0042] S301: Algorithm selection: Select a suitable data transmission algorithm based on the network environment and data characteristics, such as TCP, UDP, and UDT;

[0043] S302: Buffer size adjustment: adjusting the data transmission buffer size according to network bandwidth and delay to avoid buffer overflow and data loss;

[0044] Sending frequency adjustment: adjust the frequency of data sending according to network load and delay to avoid network congestion and data delay;

[0045] Retransmission strategy adjustment: According to the packet loss rate and network delay, adjust the data retransmission strategy, including the number of retransmissions and the retransmission time interval, to improve the reliability of data transmission;

[0046] S303: Model training: Use historical data and real-time monitoring data to train machine learning models, including neural networks and support vector machines, to improve the accuracy and intelligence of algorithm adjustments;

[0047] S4: Transmission Control Module

[0048] Function: Control the data transmission process according to the algorithm and parameters adjusted by the algorithm adjustment module to ensure accurate and reliable data transmission;

[0049] Implementation steps:

[0050] S401: Data transmission: Control the sending and receiving of data according to the adjusted algorithms and parameters to ensure the data transmission rate and reliability;

[0051] S402: Error handling: During the data transmission process, the data transmission status is monitored in real time. If an error or abnormality occurs, the error is handled in a timely manner, such as retransmitting data and adjusting transmission parameters;

[0052] S403: Feedback control: Feedback the data transmission status information to the algorithm adjustment module so that the algorithm adjustment module can adjust the algorithm and parameters in time according to the feedback information.

[0053] like Figure 2 As shown, the workflow of the above system is:

[0054] Process 1: Initialization phase

[0055] 1) Parameter setting: set the network monitoring parameters, including monitoring cycle and data collection method;

[0056] 2) Algorithm initialization: Initialize the parameters of the data transmission protocol adaptive algorithm, data compression algorithm and data retransmission strategy algorithm;

[0057] 3) System startup: start the network monitoring module, data processing module, algorithm adjustment module and transmission control module;

[0058] Process 2: Operation phase

[0059] 1) Network monitoring: The network monitoring module monitors the network status in real time, collects relevant information of network data packets at regular intervals, and sends the information to the data processing module;

[0060] 2) Data processing

[0061] ① Information reception: The data processing module receives the network status information sent by the network monitoring module, and performs data preprocessing and feature extraction;

[0062] ②Algorithm adjustment: According to the extracted feature vector, the algorithm adjustment module calls the corresponding algorithm, adjusts the algorithm and parameters of data transmission, and sends the adjustment results to the transmission control module;

[0063] 3) Data transmission

[0064] ① Protocol selection: The transmission control module selects a suitable data transmission protocol and establishes a data transmission connection based on the algorithm and parameters provided by the algorithm adjustment module;

[0065] ②Data compression and transmission: compress the data to be transmitted, and then transmit it according to the selected protocol;

[0066] ③ Error handling: During the data transmission process, the data transmission status is monitored in real time. If errors or abnormalities occur, timely error handling is performed, such as retransmitting data and adjusting transmission parameters;

[0067] 4) Feedback control

[0068] ① Status feedback: The transmission control module feeds back the status information of data transmission, such as transmission rate, packet loss rate, etc., to the algorithm adjustment module;

[0069] ②Algorithm optimization: The algorithm adjustment module optimizes the algorithm and parameters based on feedback information to adapt to changes in the network environment;

[0070] Process 3: Ending stage

[0071] System stop: When the preset end conditions are met, such as task completion, stable network environment, etc., the system stops running and releases resources.

[0072] The key algorithm in S1 above uses a network bandwidth prediction algorithm to analyze and model historical network bandwidth data and predict the trend of network bandwidth changes in the future. The model formula is:

[0073] B t =αB t-1 +(1-α)B′ t

[0074] Among them, B t represents the predicted bandwidth at the current time t, B t-1 represents the predicted bandwidth at the previous time t-1, B′ t represents the actual bandwidth at the previous time t-1, α represents the smoothing coefficient, where 0<α<1;

[0075] Example: Assume that network bandwidth data B has been collected for n time periods 1 ,B 2 ,B 3 ,……,B n , select the moving average window size as m;

[0076] According to the formula, calculate the predicted bandwidth B at the current time t t , assuming m = 5, t = 6

[0077]

[0078] The key algorithm in S2 above is to establish a data transmission protocol adaptive algorithm based on the network bandwidth prediction results and the current network environment:

[0079] The protocol selection index is S = ω 1 S 1 +ω2 S 2 +ω 3 S 3 , where S represents the protocol selection index, S 1 Represents the transmission efficiency index of the protocol, S 2 Represents the reliability index of the protocol, S 3 represents the adaptability index of the protocol, ω 1 ,ω 2 ,ω 3 Respectively represent the weight of each indicator, and ω 1 +ω 2 +ω 3 =1;

[0080] The above

[0081] The transmission efficiency index is Where R t represents the data transmission rate at the current time t, B t Indicates the predicted value of network bandwidth at the current time t;

[0082] The reliability index is S 2 =1-P t , P t Indicates the data packet loss rate at the current time t

[0083] The adaptability index is Among them, B t represents the predicted bandwidth at the current time t, B t-1 Indicates the predicted bandwidth at the previous time t-1.

[0084] Example: For protocol A, assuming ω 1 =0.3,ω 2 =0.4,ω 3 =0.3, S 1 =0.6, S 2 =0.8, S 3 =0.7, then: S A =0.3×0.6+0.4×0.8+0.3×0.7=0.73

[0085] The key algorithm in the above S3 is to establish a data compression algorithm based on the characteristics of the data and the network environment. The compression rate calculation formula is: Where C represents the compression ratio, O represents the amount of data after compression, and I represents the amount of data before compression;

[0086] For example, if the amount of data before compression is 100 bytes and the amount of data after compression is 80 bytes, the compression ratio is:

[0087] The key algorithm in the above step S4 is to establish a data retransmission strategy algorithm based on the network packet loss rate and delay, adjust the data retransmission strategy, and the retransmission number calculation formula is: Where N represents the number of retransmissions, P t represents the data packet loss rate at the current time t, and the retransmission time interval calculation formula is: Where T represents the retransmission time interval, D represents the maximum delay tolerance value of data transmission, and R t Represents the data transmission rate at the current time t.

[0088] Example: Assume P t =0.1,

[0089] Assume D = 100 milliseconds, R t =50 bytes / millisecond,

[0090] It is worth mentioning that the key algorithm in the above step S1 adopts the network bandwidth prediction algorithm and can also adopt the moving average method, and its mathematical expression is:

[0091]

[0092] Among them, B t represents the predicted bandwidth at the current time t, B i represents the actual bandwidth at historical moment i, and n represents the window size of the moving average.

[0093] Experimental example:

[0094] Network environment simulation:

[0095] Use a network simulator to build a weak network environment and set up the following different scenarios:

[0096] Scenario 1: Network bandwidth is 500Kbps, latency is 200ms, and packet loss rate is 5%;

[0097] Scenario 2: Network bandwidth is 2Mbps, latency is 100ms, and packet loss rate is 2%;

[0098] Scenario 3: Network bandwidth is 10Mbps, latency is 50ms, and packet loss rate is 1%;

[0099] Hardware equipment:

[0100] Server: configured with Intel Core i7 processor, 16 GB memory, 1 Gbps network interface, running Windows Server 2019 operating system;

[0101] Client computer: configured with Intel Core i5 processor, 8 GB memory, 1 Gbps network interface, running Windows 10 operating system;

[0102] Software Platform:

[0103] Select HTTP and UDP as data transmission protocols for testing;

[0104] Using a customized data transmission test tool, you can record parameters such as data transmission rate, delay, and packet loss rate.

[0105] In the above three different network environment scenarios, the traditional network algorithm (using TCP and UDP default settings as an example) and the adaptive algorithm of the present invention are used to perform data transmission tests. The amount of data transmitted in each test is 100MB, and various parameters in the transmission process are recorded. According to the results of the data transmission test, the performance of the adaptive algorithm of the present invention is evaluated from three aspects: transmission efficiency, reliability, and adaptability, and compared with the traditional algorithm. The comparison results are as follows:

[0106] Table 1 Transmission efficiency comparison

[0107]

[0108] It can be seen from Table 1 that in different network scenarios, the adaptive algorithm of the present invention is superior to the traditional network algorithm in terms of transmission efficiency.

[0109] Table 2 Reliability comparison

[0110]

[0111] It can be seen from Table 2 that in a network scenario with a high packet loss rate (such as scenario 1), the adaptive algorithm of the present invention can effectively reduce data packet loss and improve the success rate of data transmission.

[0112] Table 3 Comparison of adaptability

[0113]

[0114] The adaptive algorithm of the present invention can adapt to different changes in network environment. When parameters such as network bandwidth, delay and packet loss rate change, the algorithm parameters can be quickly adjusted to ensure the performance of data transmission. The fluctuation range of its transmission rate, delay and packet loss rate is smaller than that of traditional algorithms.

[0115] Embodiment 2:

[0116] A weak network self-adaptive device based on dynamic network monitoring is communicatively connected with a weak network self-adaptive algorithm system based on dynamic network monitoring.

[0117] It should be understood that although the present specification is described according to embodiments, not every embodiment contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0118] The series of detailed descriptions listed above are only specific descriptions of feasible implementation methods of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent implementation methods or changes that do not deviate from the technical spirit of the present invention should be included in the scope of protection of the present invention.

Claims

1. A weak network self-adaptation method based on dynamic network monitoring, characterized in that: The following steps are involved: By deploying monitoring agents on network nodes, the size, sending time, and receiving time of network data packets are collected, and the collected data is analyzed to calculate the network bandwidth, delay, and packet loss rate, and the analyzed network status information is used to generate a status report; as well as Extract network bandwidth, delay, and packet loss rate from the network status report, preprocess the extracted data, and extract feature vectors from the preprocessed data; as well as Adjust the data transmission buffer size according to network bandwidth and delay, adjust the data transmission frequency according to network load and delay, and adjust the data retransmission strategy according to packet loss rate and network delay; as well as According to the adjusted algorithms and parameters, control the sending and receiving of data. During the data transmission process, monitor the data transmission status in real time, provide feedback on the data transmission status information, and adjust the algorithms and parameters in time according to the feedback information. The adaptive method further includes Establish a network bandwidth prediction algorithm, analyze and model historical network bandwidth data, and predict the network bandwidth change trend in the future. The model formula is: B t =αB t-1 +(1-α)B′ t Among them, B t represents the predicted bandwidth at the current time t, B t-1 represents the predicted bandwidth at the previous time t-1, B′ t represents the actual bandwidth at the previous time t-1, α represents the smoothing coefficient, where 0<α<1; According to the network bandwidth prediction results and the current network environment, a data transmission protocol adaptive algorithm is established, and the protocol selection index is S = ω1S1 + ω2S2 + ω3S3, where S represents the protocol selection index, S1 represents the transmission efficiency index of the protocol, S2 represents the reliability index of the protocol, S3 represents the adaptability index of the protocol, ω1, ω2, ω3 represent the weights of each index respectively, and ω1+ω2+ω3=1; According to the characteristics of the data and the network environment, a data compression algorithm is established, and the compression rate calculation formula is: Where C represents the compression ratio, O represents the amount of data after compression, and I represents the amount of data before compression; According to the network packet loss rate and delay, a data retransmission strategy algorithm is established to adjust the data retransmission strategy. The formula for calculating the number of retransmissions is: Where N represents the number of retransmissions, P t represents the data packet loss rate at the current time t, and the retransmission time interval calculation formula is: Where T represents the retransmission time interval, D represents the maximum delay tolerance value of data transmission, and R t Represents the data transmission rate at the current time t.

2. According to claim 1, a weak network self-adaptation method based on dynamic network monitoring is characterized in that: The steps of the network bandwidth prediction algorithm are as follows: S1: Collect network bandwidth data over a period of time; S2: Clean and normalize the collected data; S3: Select exponential smoothing method and train the model based on historical data; S4: Use the trained model to predict the network bandwidth change trend in the future based on the actual bandwidth data at the current moment.

3. According to claim 1, a weak network self-adaptation method based on dynamic network monitoring is characterized in that: The steps of the data compression algorithm are as follows: S1: Analyze the type, distribution, and redundancy of data; S2: Select indicators according to the selected protocol and calculate the indicator values ​​of different data transmission protocols; S3: Select the optimal data transmission protocol according to the calculated protocol indicator value.

4. The method for weak network self-adaptation based on dynamic network monitoring according to claim 1, characterized in that: The steps of the network bandwidth prediction algorithm are as follows: S1: Collect network bandwidth data over a period of time; S2: Select a data compression algorithm based on the data feature analysis results; S3: Set the parameters of the compression algorithm according to the network environment and data characteristics: compression level, window size; S4: compress the data using the selected compression algorithm and set parameters.

5. The method for weak network self-adaptation based on dynamic network monitoring according to claim 1, characterized in that: The steps of the data re-strategy algorithm are as follows: S1: Real-time monitoring of changes in network packet loss rate; S2: Calculate the number and time interval of data retransmission based on the monitored packet loss rate and the preset algorithm; S3: According to the calculated retransmission strategy, control the data retransmission process to ensure reliable data transmission.

6. The method for weak network self-adaptation based on dynamic network monitoring according to claim 1, characterized in that: The network bandwidth prediction algorithm can also use a moving average method, and its mathematical expression is: Among them, B t represents the predicted bandwidth at the current time t, B i represents the actual bandwidth at historical moment i, and n represents the window size of the moving average.

7. A weak network adaptive algorithm system based on dynamic network monitoring, characterized in that: include: Network monitoring module: responsible for real-time monitoring of network status, including parameters: network bandwidth, delay, and packet loss rate; Data processing module: processes the received network status reports, extracts key information, and performs data preprocessing according to preset algorithms and rules; Algorithm adjustment module: According to the feature vector extracted by the data processing module, combined with the preset algorithm and rules, adjust the algorithm and parameters of data transmission to adapt to changes in the network environment; Transmission control module: controls the data transmission process according to the algorithm and parameters adjusted by the algorithm adjustment module to ensure accurate and reliable data transmission.

8. A weak network adaptive device based on dynamic network monitoring, characterized in that: include: The weak network adaptive device based on dynamic network monitoring is communicatively connected to the weak network adaptive algorithm system based on dynamic network monitoring described in claim 7.

9. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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