Fast Packet Classification Using Bloom Filters

While the problem of general packet classification has received a great deal of attention from researchers over the last ten years, there is still no really satisfactory solution. Ternary Content Addressable Memory (TCAM), although widely used in practice, is both expensive and consumes a lot of power. Algorithmic solutions, which rely on commodity memory chips, are relatively inexpensive and power-efficient, but have not been able to match the generality and performance of TCAMs. In this paper we propose a new approach to packet classification, which combines architectural and algorithmic techniques. Our starting point is the well-known crossproducting algorithm, which is fast but has significant memory overhead due to the extra rules needed to represent the crossproducts. We show how to modify the crossproduct method in a way that drastically reduces the memory required, without compromising on performance. We avoid unnecessary accesses to off-chip memory by filtering off-chip accesses using on-chip Bloom filters. For packets that match p rules in a rule set, our algorithm requires just 4 + p + ǫ independent memory accesses on average, to return all matching rules, where ǫ á 1 is a small constant that depends on the false positive rate of the Bloom filters. Each memory access is just 256 bits, making it practical to classify small packets at OC-192 link rates using two commodity SRAM chips. For rule set sizes ranging from a few hundred to several thousand filters, the average rule set expansion factor attributable to the algorithm is just 1.2. The memory consumption per rule is 36 bytes in the average case