Lightweight And Fine-grained Network Traffic Monitoring Mechanism And Simulation Implementation

With the deep integration and application popularization of 5G technology and IPv6 technology,the shortage of IP address resources has significantly improved,the type of traffic and the amount of data in the network have risen sharply.However,with the continuous emergence of networks,the network structure has also become very complex,and abnormal traffic is constantly increasing,which has brought great challenges to network monitoring.Existing network monitoring technologies have problems such as abnormal traffic monitoring and incomplete data acquisition on the network,which are far from meeting the requirements of fine-grained network monitoring.Based on this,the lightweight and fine-grained network traffic monitoring and method proposed in this thesis are used to monitor the real-time status of the network.When abnormal traffic information appears in the network,Internet Service Provider(ISP)can analyze the main content of this thesis.The research content and innovative work are as follows:1.Based on the analysis of existing network traffic monitoring methods and technologies,this thesis proposes a lightweight and fine-grained network traffic monitoring mechanism and method based on NetFlow network traffic monitoring technology.The existing NetFlow network flow monitoring technology cannot obtain complete data packet information,and the flow requires a large amount of resources to deploy protocols to dispatching equipment such as switches and clusters,which greatly increases a certain occupancy rate of operators.In response to this problem,this thesis proposes a network system.The network administrator queries the specific path through the monitoring bar,and guesses from all the masters,to obtain the flow information of the corresponding flow,so as to perform fine-grained monitoring of the flow.The network operation takes advanced monitoring and monitoring quantity as input.The monitoring system will determine the flow of assumptions by itself,and quickly determine the location of on-site rules.These mirroring rules reduce network monitoring while covering the scope of network monitoring.2.Based on the specific IP points in the network,this thesis designs the flow point algorithm and the local knowledge point algorithm for the purpose of monitoring the cost by consumption and obtaining the complete IP message.The complete path of the IP address comment in the network;and the far and near algorithm to be determined by the foresight algorithm.At the same time,this thesis conducts a simulation comparison and analysis on the key point sampling algorithm and the area map algorithm.Compared with other algorithms,the monitoring expenditure and income required by the algorithm proposed in this thesis can be improved.3.This thesis implements the proposed network traffic monitoring system and demonstrates the scalability of the system.The scheduling and query size of complex networks can be calculated in seconds,and a large number of mirroring rules can be generated within the limits of the current host.Finally,this thesis conducts an overall simulation and comparative analysis of the algorithm,and the simulation results prevent the network traffic of the lightweight and fine-grained measurement theory and method proposed in this thesis.