DiCuPIT: Distributed Cuckoo Filter-based Pending Interest Table

TL;DR

DiCuPIT is a distributed Cuckoo filter-based data structure for the Pending Interest Table in named data networking, significantly improving lookup speed and reducing memory usage compared to existing methods.

Contribution

The paper introduces DiCuPIT, a novel distributed PIT data structure using Cuckoo filters, enhancing scalability, speed, and memory efficiency in named data networking.

Findings

36% faster lookup time than Bloom filter-based methods

68% less memory consumption than hash table-based mechanisms

31% less memory than Bloom filter-based methods

Abstract

Named data networking is one of the recommended {\color{red}architectures} for the future of the Internet. In this communication architecture, the content name is used instead of the IP address. To achieve this purpose, a new data structure is added to the nodes of named data networking which is called Pending Interest Table (PIT). Scalability, memory consumption, and integration are the significant challenges in PIT design {\color{red} as} it needs to be updated for each packet, and it saves the name of the packet. This paper introduces a new data structure for PIT called DiCuPIT. DiCuPIT is a distributed data structure for the PIT table, {\color{red} that works} based on the Cuckoo filter and can cover the three features as above-mentioned. {\color{red} By} implementing this PIT, {\color{red} the lookup} time shows {\color{red} a 36\% reduction} compared to the methods based on the Bloom filter and 40\% based on hash tables. Moreover, the memory consumption is reduced by 68\% compared to the hash tables-based mechanisms and 31\% compared to the methods based on the Bloom filter.