## 5 Future Direction: AI-Based State Consistency Architecture  
Constructing an architecture capable of achieving an adaptive controller-to-controller synchronization rate through the integration of AI techniques within a PDLC SDN framework, as illustrated in Figure 3, necessitates the inclusion of additional components or modules beyond those detailed in Section 2 and depicted in Figure 2. We need the following additional components or modules:  
* **Synchronization Module:** This module implements an algorithm that dynamically adjusts controllers' synchronization rates based on network conditions and workload. This algorithm should be capable of learning and adapting over time using AI techniques. The distributed controllers work collaboratively under the guidance of the Synchronization Module to synchronize their activities.
* **AI Techniques:** Many recent studies have recommended RL for SDN networks such as [44; 45; 59]. There are many reasons for that, such as the abundance of data that the SDN switches can afford through the protocol of OpenFlow, and the high heterogeneity of distinct SDN domains. Moreover, SDN networks are considered complex and intricate systems. Subsequently, modeling such system accuracy is mathematically complicated. Due to the lack of restrictions on the structure or dynamicity of the network, model-free RL-based techniques are particularly appealing. They can be used in SDN networks in real-world scenarios.  
The state-action space for the controller synchronization problem is quite huge. Therefore, the DNN value function can be utilized as an approximator for synchronization policy [44; 45].  
DRL combines the benefits of RL with DNN, and it can effectively generate a control action for the target system while handling enormous input state spaces. DRL techniques can be utilized to train the adaptive synchronization algorithm. DRL is able to evolve based on interactions with the training environment without the need for significant labeled data collection, in contrast to supervised learning methods like DL [59]. These techniques enable the algorithm to analyze data, make predictions, and optimize synchronization decisions based on real-time network conditions in order to achieve intelligent automated synchronization behavior.  
* **Synchronization Monitoring**: Implement a mechanism to monitor the synchronization state between controllers. It can involve tracking performance metrics, latency measurements, and other relevant parameters to ensure effective coordination.
* **Network State Collection:** Incorporate mechanisms to monitor the network state, traffic patterns, and other relevant metrics. These data can be used as feedback for the  
Figure 3: Intelligent Physically Distributed but Logically Centralized (PDLC) inter-controller synchronization approach.  
AI synchronization algorithm, allowing it to make informed decisions based on the current network conditions.  
By integrating these components and modules, you can create an architecture that leverages AI techniques to achieve adaptive and dynamic controller-to-controller synchronization within a PDLC SDN environment.  
The solution should be tested for scalability and resiliency to design the architecture to be more scalable and resilient, capable of accommodating a growing number of controllers and maintaining synchronization in the face of network changes or failures. A potential application of interest that can serve as a use case involves applications that can tolerate some level of inconsistency, such as critical path-establishing or load-balancing applications.