## 3 Non-AI Consistency Solutions for PDLC SDN Controllers
### Adaptive Consistency Synchronization Techniques  
Adaptive consistency has recently been used in several works of literature. In ref. [13], they define an adaptive controller as \"One that can autonomously and dynamically tune its configuration in order to achieve a certain level of performance measured in predefined metrics and based on its requirements.\"  
The adaptive consistency module is a tunable consistency module that provides a configurable level of consistency. It encapsulates the distributed information complexity upon multiple controllers. Additionally, it develops a uniform interface that can alter the consistency of this information among different levels of consistency. The maintained network state information must reflect the changes in the fluctuating network environment to avoid inefficient rules in forwarding physical devices. By this model, the controllers can make collective designs and produce a single flow using measurement and calculation [13]. Table 3 compares some of the notable non-AI adaptive synchronization techniques for PDLC SDN consistency in academia. It compares these solutions based on scalability, robustness (reliability), consistency, synchronization approach, synchronization overhead, interoperability, East-Weast communication protocol, and implementation programming language. There are two types of adaptive solutions in the literature; some are focused on adaptive consistency according to a target application, such as the performance of load-balancing applications. Others are concerned with dynamically adapting the consistency level according to the deployed applications running on the distributed controllers at runtime. Different network applications have different consistency requirements. This depends on the recorded state inconsistencies and the threshold assigned to each application preference, which is called continuous adaptive consistency. Continuous adaptive consistency is explored in [18; 19; 39], where authors aimed to minimize synchronization overheads and enhance system performance and scalability. Their works confirm that traditional static approaches to eventual consistency may not be optimal in dynamic SDN environments. The work on adaptive state consistency for distributed Open Network Operating System (ONOS) controllers, as per [18], was compared to the static eventual consistency used in the ONOS approach. Instead of running for each controller replica, the anti-entropy process was run periodically at static constant intervals (each 3-5 s). That caused significant overhead and affected the system's performance and scalability. They scheduled the anti-entropy process only when there was a risk to the system consistency [18]. As per [19], adaptive state consistency in the distributed SDN control plane is introduced. In this model, strict synchronization is used for operations that significantly impact the network resources, while less important changes are periodically propagated across cluster nodes. Every resource state an SDN application uses has a consistency level (e.g., topology manager and routing). Based on the experienced state convergence after a non-synchronization time has passed and the inefficiencies brought on by operations using the stale state as inputs, the consistency level is adjusted [19]. Their model provides a transition of global controller decisions into local ones by designating all controllers as rulers of all switches. This method will reduce controller-to-controller synchronization efforts in the distributed control plane. In their concept, a single controller can apply a global route configuration. That, in current controllers, requires message delivery across the entire distributed control plane to all switches on the path, where the controller's administrative domain may span the entire network [19]. In the same idea of continuous adaptive consistency, [PERSON] and [PERSON] [40] proposed a clustering-based consistency adaptation strategy for distributed SDN controllers.