Optimal Flow User Connectivity Through Switches to Destiny Nodes in a Software-Defined Network

• Published in: 2024 IEEE International Conference on Automation/XXVI Congress of the Chilean Association of Automatic Control (ICA-ACCA) pp. 1 - 6
• Main Authors: Viveros, Andres, Adasme, Pablo, Cordero, Sergio, Juan, Enrique San
• Format: Conference Proceeding
• Language: English
• Published: IEEE 20.10.2024
• Subjects: Analytical models; and software-defined networks; Control systems; load balancing; Mathematical models; mixed-integer programming; optimal user flow; Optimization; Sensor systems and applications; Servers; Shape; Software; Software defined networking; Telemedicine
• DOI: 10.1109/ICA-ACCA62622.2024.10766753

This article examines the management of user traffic to the network access point and within the network, from the user's access point to the destination server containing the required information. This study is conducted within the architecture of Software-Defined Networks (SDN). Two models are analyzed. The first model aims to minimize latency from the user to the network access point and within the network to retrieve information from the server while also minimizing the number of controllers. This model has a quadratic relationship between switches and controllers, which could affect performance. Subsequently, the second model linearizes the quadratic relationship. The goal is to manage the network by minimizing user access latency, considering that users will select the connection point with the minimum distance from the network access points to the destination server. For our research, we used 13 real Benchmark networks subject to extensive tests using our models and the computational power of the Gurobi software to find solutions. This work represents a pioneering effort in mathematical optimization related to the problem of how to manage the flow of user data from the point where the user connects to the network. The study focuses on doing this within a network that uses SDN technology. Detailed analysis of the test results shows that the models' effectiveness is closely linked to the network's scale. Additionally, compared to the first, the second model's performance generally performs worse in larger networks. These insights offer a deep understanding of the models' functionalities and optimization dynamics. They are finally, enhancing network efficiency and performance in SDNs.