Intelligent Network Control

Finding the near-optimal control strategy is the most critical and ubiquitous problem in a network. Examples include routing decision, load balancing, QoS-enable load scheduling, and so on. However, the majority solutions of these problems are largely relying on a manual process. To address this issue, in this chapter, we apply several artificial intelligence approaches for self-learning control strategies in networks. In this chapter, we first present an energy-aware multi-controller placement scheme as well as a latency-aware resource management model for the SDWN. Moreover, the particle swarm optimization (PSO) is invoked for solving the multi-controller placement problem, and a deep reinforcement learning (DRL) algorithm aided resource allocation strategy is conceived. Then, we present a novel controller mind (CM) framework to implement automatic management among multiple controllers and propose a novel Quality of Service (QoS) enabled load scheduling algorithm based on reinforcement learning to solve the problem of complexity and pre-strategy in the networks. In addition, we present a Wireless Local Area Networks (WLAN) interference self-optimization method based on a Self-Organizing Feature Map (SOM) neural network model to suppress the interference in local area networks. Finally, we propose a BC-based consensus protocol in distributed SDIIoT, where BC works as a trusted third party to collect and synchronize network-wide views between different SDN controllers. In addition, we use a novel dueling deep Q-learning approach to solve this joint problem.