A network load balancing system and a balancing method based on deep reinforcement learning

Abstract

The invention discloses a network load balancing system and a balancing method based on depth reinforcement learning. The balancing system comprises a control plane and a data plane. The control planecomprises an INT module and a network module. The INT module obtains network information on each node in the network module by sending a detection packet and sends the network information to the control plane. The control plane comprises a DQN module, a network state information module, a shortest path routing algorithm module and a node source route path update module. The network state information module receives the network information sent by the control plane and sends the network information to the DQN module. The output action of DQN module calls dijkstra algorithm module to calculatethe optimal path, and transmits the update result of node flow table to the corresponding node equipment in the network. This scheme is based on P4's INT technology and Deep Q Network model in artificial intelligence to achieve intelligent load balancing of SDN network, so as to realize the rational utilization of network resources, effectively improve network efficiency and reduce network congestion.

Description

technical field [0001] The invention relates to the technical field of computer networks, in particular to a network load balancing method based on deep reinforcement learning. Background technique [0002] INT (In-band Network Telemetry) is a framework designed to collect and report network status. It is implemented through the data plane without the intervention of the control plane. In INT's architectural model, data packets contain header fields called "probe commands" by network devices. The instructions corresponding to these fields tell the INT-enabled device what status information it needs to collect and write this information into the INT packet. INT traffic sources (could be applications, network endpoints, etc.) can embed these commands in normal or INT packets. Similarly, the INT traffic sink (traffic sink) collects the results of these instructions to accurately monitor the status of the data plane. [0003] Reinforcement learning is an important machine lea...

AI Technical Summary

Problems solved by technology

[0013] On the other hand, ECMP cannot effectively solve the problem of network congestion caused by uneven traffic distribution

A specific manifestation is that in a network with a symmetrical topology (such as a data center network), due to the symmetry of traffic and topology, ECMP can effectively reduce network congestion, but in a network with an asymmetric topology ( For example, in the general communication network), the load balancing effect brought by the use of ECMP is very limited

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