Self-adaptive traffic prediction and dynamic network optimization system

Abstract

The invention discloses a self-adaptive traffic prediction and dynamic network optimization system, and relates to the technical field of automatic management of cloud computing networks. Comprising the following steps: step 1: carrying out data collection and processing, step 2: carrying out model training and constructing a prediction engine by utilizing a traffic prediction model, step 3: carrying out real-time monitoring and prediction, and step 4: dynamically adjusting a routing table and optimizing a routing path by utilizing a routing optimization algorithm according to predicted traffic data. The method comprises the following steps of: (1) predicting traffic data, dynamically distributing network loads to different servers or nodes according to the predicted traffic data by utilizing a load balancing algorithm to balance resource use, (5) carrying out decision support and automation: providing decision support, recommending configuration change and automatically executing the configuration change when necessary, and (6) carrying out feedback and iteration: continuously collecting effect data after execution, and performing iterative optimization on the flow prediction model fed back to the prediction engine.

Description

Technical Field

[0001] The present invention discloses an adaptive traffic prediction and dynamic network optimization system, and relates to the technical field of automated management of cloud computing networks. Background Art

[0002] Currently, network traffic management faces many challenges. First, the uncertainty and unpredictability of network traffic have increased. In particular, with the widespread application of new technologies such as cloud computing, the Internet of Things (IoT), and 5G, network traffic patterns have become more complex and changeable. Second, existing network management strategies often rely on manual configuration and empirical judgment, lack intelligence and automation, and are difficult to respond quickly to traffic changes. In addition, network congestion and latency problems are becoming increasingly serious, affecting user experience and network service quality. Summary of the invention

[0003] In view of the problems of the prior art, the present invention provides an adaptive traffic prediction and dynamic network optimization system to solve the traffic management problem in a cloud computing network environment, improve network efficiency, reduce congestion, and optimize resource allocation.

[0004] The specific scheme proposed by the present invention is:

[0005] The present invention provides an adaptive traffic prediction and dynamic network optimization method, comprising:

[0006] Step 1: Data collection and processing: Deploy network monitoring tools and sensors to collect network data in real time, and perform cleaning and standardization.

[0007] Step 2: Model training: Use time series analysis and deep learning algorithms to train traffic prediction models using historical network data, and use the traffic prediction model to build a prediction engine.

[0008] Step 3: Perform real-time monitoring and prediction: Monitor the network status in real time, input data into the prediction engine, and generate predicted traffic data.

[0009] Step 4: Perform dynamic routing and load balancing: Based on the predicted traffic data, use the routing optimization algorithm to dynamically adjust the routing table and optimize the routing path; use the load balancing algorithm to dynamically distribute the network load to different servers or nodes based on the predicted traffic data to balance resource usage.

[0010] Step 5: Perform decision support and automation: Provide decision support, recommend configuration changes, and automatically perform configuration changes when necessary.

[0011] Step 6: Provide feedback and iteration: Continue to collect performance data after execution and feed it back to the traffic prediction model of the prediction engine for iterative optimization.

[0012] Furthermore, in step 1 of the method for adaptive traffic prediction and dynamic network optimization, network monitoring tools NetFlow and / or sFlow are deployed to collect network data in real time through NetFlow and / or sFlow.

[0013] Furthermore, step 2 of the adaptive traffic prediction and dynamic network optimization method: model training, includes: performing traffic feature analysis, performing feature engineering analysis based on the collected historical network data, and extracting key features that are helpful for prediction, the historical network data includes timestamp, packet size, source and destination address; using time series analysis and deep learning algorithms to use key feature data to train the traffic prediction model.

[0014] Furthermore, in step 4 of the adaptive traffic prediction and dynamic network optimization method, dynamic routing and load balancing are performed, including: identifying congestion points in the network based on predicted traffic data, dynamically adjusting the routing table using a routing optimization algorithm, dispersing traffic from congestion points to other paths, optimizing paths, and planning backup paths.

[0015] Furthermore, when recommending configuration changes in step 5 of the adaptive traffic prediction and dynamic network optimization method, resource data is used to analyze the use and cost of network resources, predict resource requirements for different services and time periods, and dynamically adjust resource allocation based on the predicted traffic data.

[0016] The present invention provides an adaptive traffic prediction and dynamic network optimization system, including a data collection and processing module, a model management module, a prediction module, an adjustment module, a decision management module and a feedback module.

[0017] The data collection and processing module collects and processes data: deploys network monitoring tools and sensors, collects network data in real time, and performs cleaning and standardization.

[0018] Model management module for model training: using time series analysis and deep learning algorithms to train traffic prediction models using historical network data, and using traffic prediction models to build prediction engines.

[0019] The prediction module performs real-time monitoring and prediction: monitors network status in real time, inputs data into the prediction engine, and generates predicted traffic data.

[0020] The adjustment module performs dynamic routing and load balancing: according to the predicted traffic data, the routing table is dynamically adjusted using the routing optimization algorithm to optimize the routing path; the load balancing algorithm is used to dynamically distribute the network load to different servers or nodes according to the predicted traffic data to balance the use of resources.

[0021] The decision management module provides decision support and automation: it provides decision support, recommends configuration changes, and automatically executes configuration changes when necessary.

[0022] The feedback module performs feedback and iteration: continuously collects effect data after execution and feeds it back to the traffic prediction model of the prediction engine for iterative optimization.

[0023] Furthermore, the data collection and processing module of the adaptive traffic prediction and dynamic network optimization system deploys the network monitoring tools NetFlow and / or sFlow, and collects network data in real time through NetFlow and / or sFlow.

[0024] Furthermore, the model management module of the adaptive traffic prediction and dynamic network optimization system performs model training, including: performing traffic feature analysis, performing feature engineering analysis based on the collected historical network data, and extracting key features that are helpful for prediction, the historical network data including timestamp, packet size, source and destination address; using time series analysis and deep learning algorithms to train the traffic prediction model using key feature data.

[0025] Furthermore, the adjustment module of the adaptive traffic prediction and dynamic network optimization system performs dynamic routing and load balancing, including: identifying congestion points in the network based on predicted traffic data, dynamically adjusting the routing table using a routing optimization algorithm, dispersing traffic from congestion points to other paths, optimizing paths, and planning backup paths.

[0026] Furthermore, when the decision management module of the adaptive traffic prediction and dynamic network optimization system recommends configuration changes, it uses resource data to analyze the use and cost of network resources, predicts resource requirements for different services and time periods, and dynamically adjusts resource allocation based on the predicted traffic data.

[0027] The benefits of the method of the present invention are:

[0028] Improve network performance: By accurately predicting traffic patterns, network performance can be significantly improved. The system dynamically adjusts routing and load balancing strategies to ensure the optimal data packet transmission path, reduce latency and packet loss, and thus improve overall network throughput and efficiency.

[0029] Reduce network congestion: It can predict and identify potential congestion points, and disperse traffic to different paths through intelligent routing and load balancing technology, effectively avoiding network congestion and ensuring the continuity and availability of key businesses and applications. Whether it is online video streaming, online games or large-scale data transmission, users will enjoy smoother and more stable services, reducing waiting time and potential connection interruptions.

[0030] Preventing service interruptions: Through real-time monitoring and abnormal traffic detection, it can quickly respond to network failures and attacks, automatically switch to alternative paths or execute fault recovery measures, reduce the risk of service interruptions, and ensure business continuity.

[0031] Optimize resource management and reduce operating costs: Make network resources more reasonably utilized. Automatically adjust resource allocation according to real-time traffic demand to avoid resource waste and meet resource requirements in different business scenarios. Optimized resource management directly leads to reduced operating costs by reducing unnecessary bandwidth purchases and idle resources.

[0032] Improved network security: Integrated abnormal traffic detection and automatic response mechanisms enhance network security protection. The system can detect and respond to potential security threats in a timely manner, reducing the risk of network attacks and data leaks. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0034] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0035] Example 1

[0036] The present invention provides an adaptive traffic prediction and dynamic network optimization method, comprising:

[0037] Step 1: Data collection and processing: Deploy network monitoring tools and sensors to collect network data in real time, and perform cleaning and standardization processing. The network monitoring tools NetFlow and / or sFlow can be deployed to collect network data in real time through NetFlow and / or sFlow.

[0038] Step 2: Perform model training: Use time series analysis and deep learning algorithms to train the traffic prediction model using historical network data, and use the traffic prediction model to build a prediction engine.

[0039] Model training includes: traffic feature analysis, feature engineering analysis based on collected historical network data, and extraction of key features that aid in prediction. Historical network data includes timestamps, packet sizes, and source and destination addresses; and using time series analysis and deep learning algorithms to train traffic prediction models using key feature data.

[0040] Step 3: Perform real-time monitoring and prediction: Monitor the network status in real time, input data into the prediction engine, and generate predicted traffic data.

[0041] Step 4: Perform dynamic routing and load balancing: Based on the predicted traffic data, use the routing optimization algorithm to dynamically adjust the routing table and optimize the routing path; dynamic routing includes: based on the predicted traffic data, identify the congestion points in the network, use the routing optimization algorithm to dynamically adjust the routing table, disperse the traffic from the congestion points to other paths, optimize the path, and plan backup paths.

[0042] Use load balancing algorithms to dynamically distribute network load to different servers or nodes based on predicted traffic data to balance resource usage.

[0043] Step 5: Decision support and automation: Provide decision support, recommend configuration changes, and automatically execute configuration changes when necessary. When recommending configuration changes, use resource data to analyze the use and cost of network resources, predict resource requirements for different services and time periods, and dynamically adjust resource allocation based on predicted traffic data.

[0044] Step 6: Provide feedback and iteration: Continue to collect performance data after execution and feed it back to the traffic prediction model of the prediction engine for iterative optimization.

[0045] The method of the present invention improves network efficiency and response speed, reduces network congestion and potential service interruptions, ensures network stability and reliability, optimizes resource allocation and cost-effectiveness, and ensures economical and efficient operation of the network.

[0046] Example 2

[0047] The present invention provides an adaptive traffic prediction and dynamic network optimization system, including a data collection and processing module, a model management module, a prediction module, an adjustment module, a decision management module and a feedback module.

[0048] The data collection and processing module collects and processes data: deploys network monitoring tools and sensors, collects network data in real time, and performs cleaning and standardization.

[0049] Model management module for model training: using time series analysis and deep learning algorithms to train traffic prediction models using historical network data, and using traffic prediction models to build prediction engines.

[0050] The prediction module performs real-time monitoring and prediction: monitors network status in real time, inputs data into the prediction engine, and generates predicted traffic data.

[0051] The adjustment module performs dynamic routing and load balancing: according to the predicted traffic data, the routing table is dynamically adjusted using the routing optimization algorithm to optimize the routing path; the load balancing algorithm is used to dynamically distribute the network load to different servers or nodes according to the predicted traffic data to balance the use of resources.

[0052] The decision management module provides decision support and automation: it provides decision support, recommends configuration changes, and automatically executes configuration changes when necessary.

[0053] The feedback module performs feedback and iteration: continuously collects effect data after execution and feeds it back to the traffic prediction model of the prediction engine for iterative optimization.

[0054] As the information interaction and execution process between the modules of the above-mentioned system are based on the same concept as the embodiment of the method of the present invention, the specific contents can be found in the description of the embodiment of the method of the present invention and will not be repeated here.

[0055] Likewise, the benefits of the system of the present invention are:

[0056] Improve network performance: By accurately predicting traffic patterns, network performance can be significantly improved. The system dynamically adjusts routing and load balancing strategies to ensure the optimal data packet transmission path, reduce latency and packet loss, and thus improve overall network throughput and efficiency.

[0057] Reduce network congestion: It can predict and identify potential congestion points, and disperse traffic to different paths through intelligent routing and load balancing technology, effectively avoiding network congestion and ensuring the continuity and availability of key businesses and applications. Whether it is online video streaming, online games or large-scale data transmission, users will enjoy smoother and more stable services, reducing waiting time and potential connection interruptions.

[0058] Preventing service interruptions: Through real-time monitoring and abnormal traffic detection, it can quickly respond to network failures and attacks, automatically switch to alternative paths or execute fault recovery measures, reduce the risk of service interruptions, and ensure business continuity.

[0059] Optimize resource management and reduce operating costs: Make network resources more reasonably utilized. Automatically adjust resource allocation according to real-time traffic demand to avoid resource waste and meet resource requirements in different business scenarios. Optimized resource management directly leads to reduced operating costs by reducing unnecessary bandwidth purchases and idle resources.

[0060] Improved network security: Integrated abnormal traffic detection and automatic response mechanisms enhance network security protection. The system can detect and respond to potential security threats in a timely manner, reducing the risk of network attacks and data leaks.

[0061] It should be noted that not all steps and modules in the above-mentioned processes and system structures are necessary, and some steps or modules can be ignored according to actual needs. The execution order of each step is not fixed and can be adjusted as needed. The system structure described in the above-mentioned embodiments can be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or some components in multiple independent devices may be implemented together.

[0062] The above-described embodiments are only preferred embodiments for fully illustrating the present invention, and the protection scope of the present invention is not limited thereto. Equivalent substitutions or changes made by those skilled in the art based on the present invention are within the protection scope of the present invention. The protection scope of the present invention shall be subject to the claims.

Claims

1. An adaptive traffic prediction and dynamic network optimization method, characterized by include: Step 1: Data collection and processing: Deploy network monitoring tools and sensors to collect network data in real time, and perform cleaning and standardization. Step 2: Model training: Use time series analysis and deep learning algorithms to train traffic prediction models using historical network data, and use the traffic prediction model to build a prediction engine. Step 3: Real-time monitoring and prediction: Monitor network status in real time, input data into the prediction engine, and generate predicted traffic data. Step 4: Perform dynamic routing and load balancing: Based on the predicted traffic data, use the routing optimization algorithm to dynamically adjust the routing table and optimize the routing path; use the load balancing algorithm to dynamically distribute the network load to different servers or nodes based on the predicted traffic data to balance resource usage. Step 5: Perform decision support and automation: Provide decision support, recommend configuration changes, and automatically perform configuration changes when necessary. Step 6: Provide feedback and iteration: Continue to collect performance data after execution and feed it back to the traffic prediction model of the prediction engine for iterative optimization.

2. According to the adaptive traffic prediction and dynamic network optimization method described in claim 1, it is characterized in that in step 1, network monitoring tools NetFlow and / or sFlow are deployed to collect network data in real time through NetFlow and / or sFlow.

3. The method for adaptive traffic prediction and dynamic network optimization according to claim 1, characterized in that Step 2: Conduct model training, including: traffic feature analysis, feature engineering analysis based on the collected historical network data, extract key features that are helpful for prediction. The historical network data includes timestamp, packet size, source and destination addresses; use time series analysis and deep learning algorithms to train the traffic prediction model using key feature data.

4. The method for adaptive traffic prediction and dynamic network optimization according to claim 1, wherein the step 4 performs dynamic routing and load balancing, including: Based on the predicted traffic data, congestion points in the network are identified, and the routing optimization algorithm is used to dynamically adjust the routing table to disperse the traffic from the congestion points to other paths, optimize the paths, and plan alternative paths.

5. The method for adaptive traffic prediction and dynamic network optimization according to claim 1, characterized in that When the configuration change is recommended in step 5, the resource data is used to analyze the usage and cost of network resources, predict resource requirements for different services and time periods, and dynamically adjust resource allocation based on the predicted traffic data.

6. An adaptive traffic prediction and dynamic network optimization system, characterized by It includes data collection and processing module, model management module, prediction module, adjustment module, decision management module and feedback module. The data collection and processing module collects and processes data: deploys network monitoring tools and sensors, collects network data in real time, and performs cleaning and standardization. Model management module for model training: using time series analysis and deep learning algorithms to train traffic prediction models using historical network data, and using traffic prediction models to build prediction engines. The prediction module performs real-time monitoring and prediction: real-time monitoring of network status, input of data into the prediction engine, and generation of predicted traffic data. The adjustment module performs dynamic routing and load balancing: according to the predicted traffic data, the routing table is dynamically adjusted using the routing optimization algorithm to optimize the routing path; the load balancing algorithm is used to dynamically distribute the network load to different servers or nodes according to the predicted traffic data to balance the use of resources. The decision management module provides decision support and automation: it provides decision support, recommends configuration changes, and automatically executes configuration changes when necessary. The feedback module performs feedback and iteration: continuously collects effect data after execution and feeds it back to the traffic prediction model of the prediction engine for iterative optimization.

7. The adaptive traffic prediction and dynamic network optimization system according to claim 6 is characterized by: The data collection and processing module deploys network monitoring tools NetFlow and / or sFlow to collect network data in real time through NetFlow and / or sFlow.

8. The adaptive traffic prediction and dynamic network optimization system according to claim 6 is characterized by: The model management module performs model training, including: traffic feature analysis, feature engineering analysis based on the collected historical network data, and extraction of key features that are helpful for prediction. The historical network data includes timestamps, packet sizes, and source and destination addresses; and the use of time series analysis and deep learning algorithms to train traffic prediction models using key feature data.

9. The adaptive traffic prediction and dynamic network optimization system according to claim 6, characterized in that The adjustment module performs dynamic routing and load balancing, including: identifying congestion points in the network based on predicted traffic data, dynamically adjusting the routing table using routing optimization algorithms, dispersing traffic from congestion points to other paths, optimizing paths, and planning alternate paths.

10. The adaptive traffic prediction and dynamic network optimization system according to claim 6, characterized in that When the decision management module recommends configuration changes, it uses resource data to analyze the use and cost of network resources, predict resource requirements for different services and time periods, and dynamically adjust resource allocation based on predicted traffic data.

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