Intelligent QoS routing optimization method and system based on deep reinforcement learning in SDN environment

Abstract

The invention discloses an intelligent QoS routing optimization method and system based on deep reinforcement learning in an SDN environment, and the method comprises the following steps: expressing all streaming media services in a network as a service request set, and then for each request, searching a path meeting the network service quality from a streaming media server to a heterogeneous client; sequentially determining the route of each flow request, and finally constructing a multicast tree by adopting a QoS route optimization algorithm. For a network congestion link or a malicious node, the most suitable next node at present can be found for routing through a deep reinforcement learning method. By adopting the method of combining deep learning and reinforcement learning, the transmission delay of the video stream can be effectively reduced, and the accuracy of routing decision can be improved. Meanwhile, the design of a distributed control plane is adopted and can be realized in various network topologies, so that the network congestion can be avoided, the expandability of the network is improved, the interaction with a single controller is reduced, and the overall utilityof the network is improved.

Description

technical field [0001] The invention belongs to network routing optimization technology, in particular to an intelligent QoS routing optimization method and system based on deep reinforcement learning in an SDN environment. Background technique [0002] In recent years, with the vigorous development of the Internet, there have been more and more network applications, and network traffic has exploded. With the rapid growth of the network scale and the number of users, the network structure is becoming more and more complex, and network security and route optimization are facing more and more challenges. At the same time, new network application services for heterogeneous end users have emerged. For example, multimedia streaming applications such as Internet TV, online games, and video conferencing have become more and more popular on the Internet. The sharp increase in network data has led to network management equipment Variables are complex. With the improvement of the fl...

AI Technical Summary

Problems solved by technology

Approximately fitting the current network state by modeling, and using a heuristic method to request real-time calculation of routing configuration for multimedia streams, the disadvantage is that it has strict applicable scenarios, and the calculation overhead is huge, making it difficult to cope with the future real-time and highly dynamic network environment; however , multimedia streaming applications, especially real-time video streaming applications, usually have strict end-to-end delay restrictions on the transmission of video streams, so as to ensure the user's network quality service experience

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