An Efficient Packet Classification Method Based on Extended Bloom Filter and Cuckoo Hash

Abstract

The development of 5G networks has brought higher requirements for services such as switching, routing, and QoS (Quality of Service) of the carrying network and the core network. Packet classification is an essential technology which plays an important role in guaranteeing the high bandwidth and low latency of the network. Many hardware-based algorithms have been presented to meet the high requirements. However, the resource overhead on chip become greater and greater as the network scale expands so fast that it is urgent to improve the storage efficiency. Thus, an effective classification method based on extended bloom filter and cuckoo hash is proposed in this paper. It preserves the feature of the extended bloom filter by locating entries according to the comparison result of multiple hash counter groups, and further introduces the hash index comparison which slows down the increase of the counter groups greatly. In addition, a cuckoo hash with limited number of collisions and a reasonable deletion mechanism are also presented in this paper to ensure higher stability. The experimental results show that the method has the advantages of simple structure, wire-speed lookup capability, high stability, and high storage utilization.