A Novel Congestion Control Method Combining Deep Reinforcement Learning and Traditional Congestion Control

Abstract

The present invention discloses a new congestion control method combining deep reinforcement learning and traditional congestion control. The steps are as follows: Step 1: The transport layer protocol obtains the performance preference defined by the actual application according to the requirements, and delivers it to the deep reinforcement learning of the new congestion control algorithm Module; step 2: take the predefined interval as the basic unit, collect and update the round-trip delay and packet loss rate according to the response feedback to reflect the actual situation information in the network; step 3: run deep reinforcement learning based on the existing information The module and the traditional congestion control module obtain the congestion rate adjustment determined by the two modules; step 4: select and calculate the final congestion window adjustment decision according to the previously defined combination strategy, adjust the current transmission rate, and transmit data. The present invention can effectively adapt to application requirements, improve performance, and provide better fairness and convergence, and has practical value.

Description

technical field [0001] The invention belongs to the technical field of communication network congestion control, and in particular relates to a novel congestion control method combining deep reinforcement learning and traditional congestion control. Background technique [0002] With the continuous growth of WAN link bandwidth, the emergence of 4G network and 5G network applications and data centers and other scenarios, the industry's demand for congestion control algorithms has existed for a long time since it was proposed in the 1980s. Today's various network applications often have different requirements, such as low latency required for web browsing, high bandwidth required for downloading or video transmission applications, etc. A single congestion control algorithm is often difficult to adapt to all scenarios and application requirements, so that today's Linux There are more than fifteen congestion control algorithms built into the system kernel to choose from. [000...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that the existing communication network transmission layer congestion control strategy cannot take into account the requirements of high performance, adaptability, fairness and convergence, etc., the present invention provides a new congestion control method that combines deep reinforcement learning and traditional congestion control. Reinforcement learning and traditional congestion control work together to make decisions together, absorb the advantages of both, solve existing problems, effectively adapt to the needs of applications, and finally improve performance; and in the actual situation where CUBIC is widely deployed in the current network, it can provide more Excellent fairness and convergence, with practical value

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