Joint Traffic Control and Multi-Channel Reassignment for Core Backbone Network in SDN-IoT: A Multi-Agent Deep Reinforcement Learning Approach

Abstract

Channel reassignment is to assign again on the assigned channel resources in order to use the channel resources more efficiently. Channel reassignment in the Software-Defined Networking (SDN) based Internet of Things (SDN-IoT) is a promising paradigm to improve the communication performance of the network, since it allows software-defined routers (SDRs) with the help of SDN controller to appropriately schedule the traffic loads to meet the better transaction of corresponding channels in one link. However, the existing channel reassignment works have many limitations. In this paper, we develop a joint multi-channel reassignment and traffic control framework for the core backbone network in SDN-IoT. Comparing to classic performance metrics, we design a more comprehensive objection function to maximize the throughput and to minimize packet loss rate and the time delay by scheduling the appropriate traffic loads to corresponding channels in one link. We develop a Multi-Agent Deep Deterministic Policy Gradient (MADDPG)-based traffic control and multi-channel reassignment (TCCA-MADDPG) algorithm to optimize the objection function to achieve traffic control and channel reassignment. To tackle the dynamics and complexity of the core backbone network, we use the traffic prediction result as the part of the channel state information. In order to make better use of the time continuity of the channel state, we add an LSTM layer to the neural network in the experiment to capture the timing information of the channel. Simulation results show that the proposed algorithm converges faster and outperform existing methods.