Hierarchical hybrid search structure for high performance packet classification

Hierarchical search structures for packet classification offer good memory performance and support quick rule updates when implemented on multi-core network processors. However, pipelined hardware implementation of these algorithms has two disadvantages: (1) backtracking which requires stalling the pipeline and (2) inefficient memory usage due to variation in the size of the trie nodes. We propose a clustering algorithm that can partition a given rule database into a fixed number of clusters to eliminate back-tracking in the state-of-the-art hierarchical search structures. Furthermore, we develop a novel ternary trie data structure (T∈). In T∈ structure, the size of the trie nodes is fixed by utilizing ∈-branch property, which overcomes the memory inefficiency problems in the pipelined hardware implementation of hierarchical search structures. We design a two-stage hierarchical search structure consisting of binary search trees in Stage 1, and T∈ structures in Stage 2. Our approach demonstrates a substantial reduction in the memory footprint compared with that of the state-of-the-art. For all publicly available databases, the achieved memory efficiency is between 10.37 and 22.81 bytes of memory per rule. State-of-the-art designs can only achieve the memory efficiency of over 23 byte/rule in the best case. We also propose a SRAM-based linear pipelined architecture for packet classification that achieves high throughput. Using a state-of-the-art FPGA, the proposed design can sustain a 418 million packets per second throughput or 134 Gbps (for the minimum packet size of 40 Bytes). Additionally, our design maintains packet input order and supports in-place non-blocking rule updates.