Research On Network Congestion Control Protocol Based On Deep Reinforcement Learning

With the rapid development of the mobile Internet and the industrial Internet,the network is ever-changing and its complexity has greatly increased.At the same time,the data explosion has caused the amount of data that needs to be transmitted in the network to continue to increase.Once the data volume of a certain node exceeds the carrying value,Network congestion occurs.Then,the user will get stuck and it will cause economic losses.Traditional congestion control protocols are specifically designed for specific networks through complex calculations.When network changes intensely,traditional congestion control protocols can hardly achieve good congestion control effects.Therefore,it is necessary to deal with the new complex network by researching reliable congestion control protocol.Aiming at the new problems faced by network congestion control technology,this paper combines deep reinforcement learning technology to design a reliable and efficient congestion control protocol that can independently learn to follow network changes.This paper first analyzes the basic principles,simple application methods and commonly used algorithms of deep reinforcement learning.Then introduces the basic concepts and operating principles of two congestion control technologies and gives the methods of deep reinforcement learning applied to network congestion control.Afterwards,this paper combines the basic elements of deep reinforcement learning to give the specific design of the system under congestion control,and realizes the rate adjustment of the system through neural network and reward function to achieve the effect of congestion control.It is worth nothing that this article also builds a simplified training network that eliminates irrelevant factors,so that the system can train faster and better.Finally,this paper conducts a simulation test of the congestion control algorithm based on the Mininet network simulator,and analyzes its advantages in distinguishing congestion types and adapting to changing networks through comparison with traditional congestion control algorithms.At the same time,the robustness of the system in terms of bandwidth,delay,queuing,and packet loss is further analyzed to confirm that it can stably and reliably achieve efficient congestion control.Simulation results show that the algorithm proposed in this paper can achieve better congestion control effects than traditional algorithms.