application of deep learning algorithms in optimal controller placement in software-defined networks

Software-defined networks (sdns) have emerged as a pivotal technology in the management and control of telecommunication and communication networks. these networks offer the flexibility to dynamically configure and control network resources, optimizing their performance and efficiency. one critical aspect of sdns is the placement of controllers, which are responsible for making intelligent decisions about network traffic. this article explores the application of deep learning algorithms to optimize the placement of controllers in sdns, with the primary goal of enhancing network responsiveness, reducing latency, and improving overall network performance.