Latency Reduction and Performance Optimization in Edge-Enabled Networks

Abstract

The growing use of modern applications such as autonomous systems, augmented reality, smart healthcare, and industrial automation has created a strong demand for communication networks that can provide very fast response times and high performance. These applications need data to be processed almost instantly, with very little delay. However, traditional cloud-based systems often struggle to meet these needs because they depend on centralized data centers, where data must travel long distances. This can lead to delays, increased latency, and reduced efficiency. To overcome these challenges, edge-enabled networks have emerged as an effective solution. In this approach, computing resources are placed closer to the end users, allowing data to be processed locally instead of being sent to distant cloud servers. This helps reduce delays and improves overall network performance. This paper provides a detailed review of different methods used to reduce latency and improve performance in edge-enabled communication networks. It discusses the overall network architecture, important supporting technologies, and key techniques such as task offloading, efficient resource allocation, and intelligent routing. The study also includes a review of recent research in this field to understand current developments and trends. In addition, it highlights major challenges like scalability, security, and energy consumption that need to be addressed. Tables are included to compare different techniques and performance measures. Finally, the paper outlines future research directions that can help in developing more efficient, reliable, and scalable edge-based communication systems.