The Internet landscape has witnessed a significant shift toward Information Centric Networking (ICN) due to the exponential growth of data-driven applications. Similar to routing tables in TCP/IP architectures, ICN uses Forward Information Base (FIB) tables. However, FIB tables can grow exponentially due to their URL-like naming scheme, introducing major delays in the prefix lookup process. Existing explicit FIB aggregation solutions are very complex to run, and ICN 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 additional 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

翻译：随着数据驱动应用的指数级增长，互联网格局已显著向信息中心网络（ICN）演进。与TCP/IP架构中的路由表类似，ICN使用转发信息库（FIB）表。然而，由于其类URL的命名机制，FIB表可能呈指数级增长，导致前缀查找过程产生显著延迟。现有的显式FIB聚合方案运行复杂度极高，而依赖泛洪机制并为所有请求建立路由的ICN按需路由方案（通过发现机制减少FIB记录数以缩短查找时间）则引入了额外的可扩展性挑战。本文提出SAMBA——一种基于近似转发的自学习机制，该机制通过为给定前缀选择最接近的FIB字典树记录来减少发现次数，从而维持FIB表的小型化。通过为指定名称前缀选取最邻近前缀，SAMBA采用隐式前缀聚合（IPA）技术，隐式聚合FIB记录并降低所需的自学习发现次数。结合近似转发机制，SAMBA能够实现高效且可扩展的转发。