Zhirong Identification Network Status Prediction and Congestion Control System

Abstract

The intelligent identification network status prediction and congestion control system proposed by the present invention is a method and system for network status prediction and data packet queue congestion control based on P4 combined with machine learning model methods on the programmable data plane, through in-band network telemetry (In-band network telemetry, INT) technology collects network status feature information in real time, and adopts "LSTM-fuzzy clustering" which combines long short-term memory network (Long Short-Term Memory, LSTM) neural network model and fuzzy clustering algorithm. The model method realizes the prediction of the characteristic value of the network state. According to the fuzzy clustering of the obtained characteristic value of the network state, it is divided into four network states: normal state, congestion warning state, continuous congestion state and congestion relief state. Corresponding strategies are formulated for different network states , the controller sends corresponding flow tables and formulates switch actions under different network states, and provides a comprehensive and dynamic queue feedback mechanism to ensure that the packet queuing delay is as small as possible to achieve the effect of congestion control.

Description

technical field [0001] The invention relates to the field of programmable data plane switch and communication technology, in particular to a network state prediction and congestion control system of Zhirong identification. Background technique [0002] With the rapid development of the Internet in recent years, the number of network users has increased day by day, and the network scale has continued to expand. Especially now that various new network business forms have appeared in large numbers, the problem of network congestion has intensified. In the current network research, how to avoid network congestion, improve network communication performance, optimize the configuration of network resources, and ensure network reliability, real-time performance, stability, robustness and other performances has always been an important issue in the academic and industrial circles. Hot spots of concern. The current congestion control mechanism is composed of end-to-end transmission c...

AI Technical Summary

Problems solved by technology

[0006] On the one hand, this technology needs to issue statistical policies to the traffic acquisition module of the forwarding layer to obtain traffic during use, which brings a large bandwidth and time overhead; on the other hand, it only predicts and analyzes whether there are disturbances in the network and Whether it is running normally, to achieve congestion control and load balancing, lack of analysis of the relationship between prediction feature results and their impact on network status, and the contingency of various factors cannot be avoided

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