A Comparative Performance Analysis of Popularity-Based Caching Strategies in Named Data Networking

Abstract

Data communication in the present Internet paradigm is dependent on fixed locations that disseminate similar data several times. As a result, the number of problems has been generated in which location dependency is the most crucial for communication. Therefore, Named Data Networking (NDN) is a new network architecture that revolutionized the handling gigantic amount of data generated from diverse locations. The NDN offers in-network cache which is the most beneficial feature to reduce the difficulties of location-based Internet paradigms. Moreover, it mitigates network congestion and provides a short stretch path in the data downloading procedure. The current study explores a new comparative analysis of popularity-based cache management strategies for NDN to find the optimal caching scheme to enhance the overall network performance. Therefore, the content popularity-based caching strategies are comparatively and extensively studied in an NDN-based simulation environment in terms of most significant metrics such as hit ratio, content diversity ratio, content redundancy, and stretch ratio. In this analysis, the Compound Popular Content Caching Strategy (CPCCS) has performed better in terms to enhance the overall NDN-based caching performance. Therefore, it is suggested that the CPCCS will perform better to achieve enhanced performance in emerging environments such as, Internet of Things (IoT), Fog computing, Edge computing, 5G, and Software Defined Network (SDN).