Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale IP Backbone Networks

The end-to-end network traffic information is the basis of network management in large-scale IP backbone networks. To obtain exact network traffic data, a prevalent idea is to employ NetFlow or sFlow on all routers of the network. However, this method not only increases operational expenditures, it also affects the network load. Motivated by this issue, we propose an optimized traffic measurement method based on reinforcement learning in this paper, which can collect most of the network traffic data by activating NetFlow on a subset of interfaces of routers in a network. We use the Q- learning-based approach to deal with the problem of the interface-selection, and propose an approach to compute the reward. Furthermore, a modified Q- learning approach is proposed to handle the problem of interface-selection. The method is evaluated by the real data from the Abilene and GEANT backbone networks. Simulation results show that the proposed method can improve the efficiency of traffic measurement distinctly.