# Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization
## 6 Conclusions  
Heuristic algorithms and specific routing and traffic-related algorithms are key components when various challenges regarding Software-Defined Networks (SDNs) are addressed. As can be seen, each algorithm category has advantages over others and also has limitations to be considered, but their overall scope is to contribute to performance, reliability, and adaptability improvement for the infrastructure they serve. Choosing an algorithm over another one is a matter of how the network is deployed, what objectives are targeted to be accomplished over that specific network, and what type of data will pass the network (requirements). The general algorithms presented are better preferred for their flexibility in solving complex problems and because they have vast applicability. Latency optimization-related algorithms are preferable to be used on networks where the requirements impose low delays due to the specificity of the used applications over the network (ex: VoIP). Load balancing algorithms ensure efficient resource utilization and high availability and are used especially in network environments that are passing large amounts of data. It is known that Software-Defined Networks are environments that in most cases are in continuous adaptation and optimization, and the algorithms presented offer very helpful tools meant to address the challenges and complexities associated with the modern networking trends. After the research in the field of review works related to the applied presentation of various algorithms that can be used in the context of SDN, it appears that our work brings an important element that helps people interested in this field to have a clear vision of the variants from which they can choose to be able to experiment exactly on the metrics or cases that need to be implemented. The aforementioned comparative table and the research showcased offer a proper approach and understanding of the algorithms' capabilities and scopes that could provide network architects, administrators, or operators the possibility to optimize their environments to meet the demands of different applications and user requirements. In conclusion, the study has provided an extensive overview of various algorithmic approaches suitable to SDN networks including traditional, heuristic, and advanced artificial intelligence methods such as machine learning. The presented algorithms have key roles in enhancing security, scalability, and network performance. Even though the technology accomplished notable advancements, several open challenges remain. One of them is \begin{table}
\begin{tabular}{c c} \hline \hline
**Limitation** & **Description** \\ \hline \multirow{3}{*}{Breadth vs. Depth} & While the paper covers a wide range of algorithms, the depth of discussion on each algorithm might be limited due to the broad scope. Future work could focus on the in-depth analyses of specific algorithms. \\ \hline \multirow{3}{*}{Real-World Implementation} & The practical implementation and real-world performance of some algorithms may not be extensively covered. More empirical studies and case studies could address this gap. \\ \hline \multirow{3}{*}{Evolving Technologies} & The rapid evolution of SDN and related technologies like 5G and IoT means that new algorithms and approaches are continuously emerging. Keeping the review up-to-date with the latest advancements is a challenge. \\ \hline \multirow{3}{*}{Performance Metrics} & Different studies use various metrics to evaluate performance, making direct comparisons difficult. Standardized metrics and benchmarks would help in better comparative analysis. \\ \hline \multirow{3}{*}{Scalability and Complexity} & The scalability and computational complexity of algorithms are critical in real-world applications, but may not be thoroughly addressed for each algorithm in this review. \\ \cline{1-1}  & \\ \end{tabular}
\end{table} 
Table 8: Limitations related to the present research.