IMPROVING NAMED DATA NETWORKING PERFORMANCE THROUGH LABEL SWITCHING AND ADAPTIVE TRANSPORT METHODS

Abstract

Named Data Networking (NDN) is a novel internet architecture that focuses on data names rather than locations. However, it is still in its early stage, and no strong efficiency mechanisms have been systematically designed, measured, or tracked in data retrieval and multi-path detection. This paper proposes an approach that combines label switching with adaptive transport methods to improve the speed of packet forwarding in NDN networks. The approach involves color-coding interfaces, ranking them based on response time, and selecting the best interface for forwarding interest packets. The proposed approach was evaluated and demonstrated significant improvements in response time, throughput, and data recovery time by 12%, 10%, and 5%, respectively. This paper also provides an overview of NDN network forwarding, including Content Store (CS), Pending Interest Table (PIT), and forwarding table (FIB). Naming Label Switching (NLS) is also discussed as a method to reduce the total time for packet forwarding. The proposed approach highlights the potential of NDN and proposes an optimization method to improve its performance, which can benefit network routing and communication, communication security, and privacy.