## 4 AI-Based Solutions for SDN Consistency Synchronization
### AI-Based SDN-Related Work  
In ref. [21], they introduced the network AI architecture. The model comprises three planes: the AI plane, the control plane, and the forward plane. The generation of policies is the role of the AI plane. The AI plane benefits from SDN and monitor approaches to achieve a comprehensive view and control of the whole network [21]. In refs. [52; 53], they use the knowledge plane with the other SDN planes. The researchers add a data-driven plane to SDN networks that use the network data generated from the infrastructure to permit network intelligence. Their work advises a general design of data-driven SDN and also gives inspiration for the future evolution of SDN. The researchers in [54] proposed a data-driven intelligent future network design that integrates a big data engine in the SDN control plane. The big data engine is responsible for processing the data, analyzing the data, and supporting decisions. Even though the network architecture in this design is constricting for addressing the future Internet content delivery, the data of the big data engine are gained from both application data and network data. The researchers in [55] proposed a multi-agent controller to enable cognition in software-defined networks. This work enables the intelligence in SDN controllers in order to introduce a knowledge-defined network. The researchers constructed a multi-agent system SDN controller (MAS-SDN). The proposed architecture is composed of two constituents. First, the southbound interface can communicate between the SDN MAS and the network. Second is the multi-agent system, which consists of multi-agents communicating together. In ref. [55], they justify not using SDN controllers based on ML techniques. The ML can be used to solve a specific issue like flow classification or load balancing. Moreover, this type of work needs large sets in order to train the model. Their solution aims to construct an SDN controller that can think intelligently according to the domain knowledge (rules) and the existing beliefs (conditions). Additionally, in the future, they will be able to build a full cognitive SDN controller when logic-based solutions and machine learning come together. The researchers in [56] proposed machine learning for network resiliency and consistency. Without consistency, they argued that security could not exist. Implementing security policies on a network might be difficult when there is no way to identify any misconduct [56]. The proposed work claimed to be the first effort at an AI-based consistency verification solution. They target more resilient networks in a privacy-preserving architecture. In their study, they use DL to categorize whether the flow-table entries of the network nodes are consistent with the controller's view of the network. The system uses a hybrid approach with both controller-level centralized processing and node-level distributed processing across the data plane [56]. The extracted features are entered into a hash process. Then, the vector output is forwarded to the controller level to be verified with the controller network view. If an inconsistency is discovered, a second layer of verification is triggered to identify the event that causes the flow-based attack [56]. In light of recent achievements in using RL methods to address challenging issues, the researchers in [44] try to answer how to get the maximum synchronization benefits among controllers to preserve a logically centralized view under eventual consistency. They introduced a DQ Scheduler: deep reinforcement learning-based controller synchronization in distributed SDN. They considered the problem of synchronization among controllers as a Markov Decision Process (MDP) by developing the Deep-Q (DQ) Scheduler. They formulate the controller policy synchronization development to maximize the MDP performance metric as an MDP that can be solved by employing RL techniques. They utilize DNN to generalize and approximate the estimation of the synchronization policy.