SAMBA: Scalable Approximate Forwarding for NDN Implicit FIB Aggregation

The rapid growth of data-driven applications has prompted a shift towards Information-Centric Networking (ICN) in the Internet landscape. Like TCP/IP's routing tables, ICN employs Forward Information Base (FIB) tables. However, unlike IP addresses, the URL-like naming scheme in ICN can cause FIB tables to grow exponentially, leading to delays in prefix lookups. Current solutions use computationally intensive explicit FIB aggregation method, or on-demand routing schemes, which use a discovery mechanism to help reduce the number of FIB records and thus have shorter lookup times, rely on flooding-based mechanisms and building routes for all requests, introducing further scalability challenges. In this paper, we propose SAMBA, an Approximate Forwarding-based Self Learning, that uses the nearest FIB trie record to the given prefix for reducing the number of discoveries thus keeping the FIB table small. By choosing the nearest prefix to a given name prefix, SAMBA uses Implicit Prefix Aggregation (IPA) which implicitly aggregates the FIB records and reduces the number of Self Learning discoveries required. Coupled with the approximate forwarding, SAMBA can achieve efficient and scalable forwarding. We demonstrate that SAMBA can help reduce lookup times by up to 45%. SAMBA also implements multipath discovery and consumer-controlled flooding mechanisms, which help minimize networking overhead. Our simulation results show that SAMBA reduces the FIB table size twenty fold compared to traditional Self Learning schemes.