## 3 Non-AI Consistency Solutions for PDLC SDN Controllers
### Discussion and Open Issues
The common controller designs ONOS [24] and ONIX [22] utilize static consistency synchronization techniques (see Table 1). They offer APIs for choosing either a strong or eventual mode of consistency for their distributed state primitives, in an effort to address the adaptive consistency and scalability issue. However, the SDN application must have the active model of state consistency hard-coded because it does not change at runtime and cannot be deployed without knowing the precise network limitations [19]. However, ONOS [24], a cutting-edge SDN controller, uses the anti-entropy protocol to achieve eventual consistency. The anti-entropy protocol's main idea is that controllers randomly synchronize with one another using a gossip algorithm. Recently, more concerns have been raised about the effectiveness of their protocol despite the fact that it can eventually reach consistency [44]. Additionally, the static consistency solutions introduced in the literature have a number of shortcomings. First, they are unable to handle an SDN system that is heterogeneous and created by various vendors. Second, their consistency approach is straightforward in that it ignores the trade-off between the consistency level and SDN network performance. Third, most existing solutions in the literature try to build a consistent control plane by incorporating additional features for synchronization across the control plane. The main disadvantage of this approach is the overhead on the controllers. On the other hand, avoiding the increasing overhead on controllers by letting switches synchronize their behaviors has a drawback as they need an alteration of the SDN protocol. The use of a tunable adaptive consistency module that provides a configurable level of consistency needs to be considered. Adaptive synchronization offers several benefits, including the following:  
**Adaptability**: By dynamically adjusting the synchronization rate, the distributed controllers can effectively respond to network environment changes. For example, during periods of high network activity or frequent events, the synchronization rate can be increased with respect to the application SLA to ensure the timely propagation of updates and better responsiveness. Conversely, during periods of relative network stability, the synchronization rate can be reduced to minimize overhead and conserve resources. **Scalability**: Adaptive synchronization helps improve scalability in distributed SDN controllers. A fixed synchronization rate may become inadequate as the network grows in size or complexity, and it can irritate the network with unneeded messages. By dynamically adjusting the synchronization rate, the system can adapt to the changing demands of the network, ensuring efficient resource utilization and acceptable performance levels. However, there are several works on adaptive consistency in the literature. It is not considered fully adaptive. It needs to be more flexible and automated to any network environment and make no network assumptions. The studies on adaptive synchronization rates, such as [13; 19], suggest a dynamic adaption of synchronization rates among controllers. This is similar to the goal of other methodologies that use DRL, such as [44; 45], which suggest far more adaptability than these efforts. It makes no network assumptions and automates policy learning under any network setting. The comparison in Tables 1-3, as well as the literature, shows that there are many ways to construct a PDLC SDN architecture; some of these methods performed better than others in some areas while falling short in others. Indeed, none of the suggested SDN controller platforms addressed all the issues needing to be solved for a successful adaptive synchronization for distributed SDN deployment.