SDN network intelligent routing data transmission method based on distributed deep reinforcement learning

Abstract

The invention discloses an SDN (Software Defined Network) intelligent routing data transmission method based on distributed deep reinforcement learning, which realizes calculation of a fast routing path, maximizes throughput under the condition of ensuring delay, and solves the problems of low speed and small throughput of the traditional algorithm. According to the method, a reinforcement learning algorithm is used, the routing calculation process is simplified into simple input and output through the algorithm, multiple iterations during calculation are avoided, so that rapid calculation ofthe routing path is achieved, forwarding delay is reduced through acceleration of the speed of the routing algorithm, and data packets originally abandoned due to ttl expiration have higher probability of survival and are forwarded successfully so that network throughput is increased. During two training stages of offline training and online training is set, parameters are updated in a dynamic environment to select an optimal path, so that the invention has topology adaptability.

Description

technical field [0001] The invention belongs to the field of data transmission, and in particular relates to an SDN network intelligent routing data transmission method of distributed deep reinforcement learning. Background technique [0002] The current information technology has entered a mature stage. In the SDN network (Software Defined Network) architecture, the data flow is flexible and controllable, and the controller has a view of the entire network and can perceive network status changes in real time (such as traffic distribution, congestion status, and link utilization. situation, etc.), in reality, the routing problem is often solved by the shortest path algorithm, some simple network parameters (such as path hops, delay, etc.) The minimum path is taken as the final goal of the algorithm. A single metric and optimization goal can easily lead to congestion of some key links, resulting in unbalanced network load. Although the shortest routing algorithm based on La...

AI Technical Summary

Problems solved by technology

A single metric and optimization goal can easily lead to congestion of some key links, resulting in unbalanced network load

Although the shortest routing algorithm based on Lagrangian relaxation can find the optimal path with complex multi-constraint conditions when assigning multiple service paths, this type of heuristic routing algorithm must go through multiple iterations to calculate the optimal path, and the convergence speed Slow, poor timeliness, low throughput

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