عبدالله السلامي

While software-defined networking (SDN) offers flexibility and excellent programmability, it faces challenges in optimizing traffic engineering to adapt to changing network conditions. Enhancing SDN functionality relies heavily on effective traffic allocation and resource efficiency. Therefore, the necessity for an efficient, scalable, and adaptive algorithm is crucial to enable intelligent flow control and network traffic improvement. Despite numerous proposed solutions within this realm, the absence of a precise and efficient algorithm for managing traffic in SDN networks remains apparent due to the complexity of the problem. The importance and necessity of implementing traffic engineering based on congestion intelligence for (SDN) was clearly identified. SDN provides centralized control and flexibility for swarm intelligence algorithms, achieving optimal paths and balanced traffic distribution. Benefits include reduced latency, improved bandwidth efficiency, and increased network stability, but challenges include algorithm complexity and fast data processing. In this study, an enhanced ACO algorithm was implemented for traffic management in an SDN network. The simulation results are presented, including graphs for the average degree of nodes and network analysis. This research eva‎luates the Swarm Intelligence-based Traffic Engineering (SITE) approach for SDN using the AGIS network topology and parameters. By dynamically adjusting routes, ACO can reduce the average latency experienced by packets 30ms convergence time is 60.3s of ACO for SDN Traffic Engineering refers to the duration required for the algorithm to the reach a stable, near-optimal solutions The ACO algorithm significantly enhances network traffic distribution by identifying near-paths and promoting load balancing. The SITE approach, utilizing the collective intelligence of a large number of agents, offers a more scalable and flexible solution compared to traditional traffic engineering methods, making it well-suited for modern SDN environments .Compares (ACO) and Traditional Algorithms in network performance metrics the throughput 95mbps whit Aco and in traditional alorithm 90mbps so ACO is better in 5% throughput.And in latency with ACO 20ms and in traditional alorithm 25ms so ACO is better in 5% at latency.And in packet loss with ACO 0.5% and in traditional alogorithm 1% ACO is better in 0.5 form the traditional algorithms .And in convergence time With ACO 15ms And in traditional algorithms 30ms You can notice the difference between the two methods and know that it is better to use ACO For better improvements

مهندسي ترافيك (TE) , شبكه هاي نرم افزاري تعريف شده (SDN) , هوش ازدحام (SI) , الگوريتم بهينه سازي كلوني مورچه ها (ACO)

Traffic Engineering (TE , Software Defined Networks (SDN) , Swarm Intelligence (SI) , Ant Colony Optimization Algorithm (ACO)

Abdollah Asalami

Dr. Naser Mozayeni