Enhanced caching mechanisms for named data networking

The rapid growth of the Internet has shifted its primary use towards content distribution, prominently through the Named Data Network (NDN). As a pivotal component of Information-Centric Networking (ICN) for the Future Internet, there’s a pressing need to develop effective caching strategies for NDN. As routers face challenges with memory constraints, this research introduces the Alternative Caching Strategy (ACS) to address content replacement and updating. The ACS integrates two core mechanisms: The Content Replacement Mechanism (CRM) and the Content Update Mechanism (CUM). Both mechanisms employ Fuzzy Set Theory and Fuzzy Multiple Attribute Decision Making (MADM). Specifically, the CRM uses the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), while the CUM adopts the Simple Additive Weighting (SAW) Method. The performance of ACS is evaluated through simulations and compared across various topologies with the conventional First-In-First-Out (FIFO), Least Recently Used (LRU), Least Frequently Used (LFU), and Cache Content Popularity (CCP) strategies. ACS demonstrated significant improvements in hit ratio, throughput, and reduced hops and server load. Specifically, the enhancements in average hit ratio are 43%, 30%, 21%, and 10% sequentially, throughput enhancements are 38%, 23%, 16%, and 9%, and the averages of reduction hops number are 21%, 18%, 14%, and 8%. Finally, in the average server load, ACS registered the highest enhancement with 22% from FIFO and the lowest with 10% from CCP. The ACS presents a promising approach for the future of Internet content distribution, particularly in the realm of IoT technology deployment.