Clustered Hierarchical Search Structure for Large-Scale Packet Classification on FPGA

Most current SRAM-based high-speed Internet Protocol (IP) packet classification implementations use tree traversal and pipelining. However, these approaches result in inefficient memory utilization. Due to the limited amount of on-chip memory of the state-of-the-art Field Programmable Gate Arrays (FPGAs), existing designs cannot support large filter databases arising in backbone routers and intrusion detection systems. Hierarchical search structures for packet classification exhibit good memory performance and support quick rule update. However, pipelined hardware implementation of these algorithms suffer from inefficient resource and memory usage due to variation in the size of the trie nodes and backtracking. We propose a memory efficient organization denoted Clustered Hierarchical Search Structure (CHSS) for packet classification. We present a clustering algorithm that partitions a given filter database to reduce the memory requirement. We show that, using the resulting structure, backtracking is not needed to perform a search. We introduce two parameters (NR_trie, NR_tree), which can be chosen based on the given filter database to achieve good memory efficiency. Our algorithm demonstrates substantial reduction in the memory footprint compared with the state-of-the-art. For all publicly available filter databases, the achieved memory efficiency is between 21.54 and 41.25 bytes per rule. We map the proposed data structure onto a linear pipeline architecture to achieve high throughput. Post place and route result using a state-of-the-art FPGA device shows that the design can sustain a throughput of 408 million packets per second, or 130.5 Gbps (for the minimum packet size of 40 Bytes).