Maximizing Router Efficiency in Named Data Networking with Machine Learning-Driven Caching Placement Strategy
Ridha Muldina Negaral, Nana Rachmana Syambas, Eueung Mulyana, Novan Purba Wasesa · 2024
The demand for multimedia content imposes considerable strain on IP technology, particularly in terms of latency. Named Data Networking (NDN) emerges as a future networking solution, characterized by a content-centric approach featuring storage capabilities on routers. However, challenges persist in effectively managing the limited storage capacity of routers, necessitating the development of efficient content replacement strategies. Traditional caching replacement algorithms often struggle to adapt to the dynamic nature and popularity of content across the network. Previous studies have proposed leveraging machine learning (ML) to enhance caching replacement strategies, ensuring eviction processes target only unpopular content. These endeavors have demonstrated promising results in enhancing router storage efficiency. Nonetheless, a critical inquiry arises regarding integrating ML models within the network: Should the ML model be deployed directly on routers or exclusively at the producer level? Bridging the gap between theoretical research and practical implementation is imperative. This study is dedicated to implementing ML algorithms on routers within the NDN. It shows how a simple K-Nearest Neighbors (KNN) machine learning algorithm combined with the Least Recently Used (LRU) caching strategy could greatly improve the process of replacing NDN cache when embedded in all NDN routers. The proposed KNN-LRU model works better than common algorithms like LRU, FIFO, and LFU in terms of cache hit ratio, latency, and link load when validated using the ICARUS caching simulator.