Methods and Algorithms for Optimizing Network Traffic in Next-Generation Networks: Strategies for 5G, 6G, SDN, and IoT Systems

Abstract

The exponential growth in network traffic due to emerging technologies like 5G, 6G, Software-Defined Networking (SDN), and the Internet of Things (IoT) necessitates innovative optimization techniques to manage the increased demand. Modern networks are expected to support ultra-reliable low-latency communications, high data throughput, and seamless connectivity across millions of devices, creating unprecedented challenges for network performance and resource management. Managing and optimizing traffic in such networks poses significant challenges due to the massive increase in connected devices, fluctuating traffic demands, and the diversity of applications. This paper examines the methods and algorithms designed to optimize traffic in next-generation networks, focusing on congestion control, load balancing, energy-efficient routing, and resource allocation. The role of SDN in enhancing network flexibility and programmability is discussed, alongside the increasing use of artificial intelligence (AI) and machine learning (ML) for real-time traffic optimization. The paper also addresses the distinct challenges of IoT networks, where traffic patterns are irregular, and devices have stringent energy constraints. The objective of this paper is to provide a review of how traffic optimization techniques are reshaping the domains of modern networks and enabling more efficient, reliable, and scalable communication systems.