Abstract-A wide variety of packet classification algorithms and devices exist in the research literature and commercial market. The existing solutions exploit various design tradeoffs to provide high search rates, power and space efficiency, fast incremental updates, and the ability to scale to large numbers of fitters. There remains a need for techniques that achieve a favorable balance among these tradeoffs and scale to support classification on additional fields beyond the standard 5-tuple. We introduce Dishibuted Crossproducting of Field Labels (DCFL), a novel comhination of new and existing packet classification techniques that Ieverages key observatiom of the structure of real filter sets and takes advantage of the capabilities of modern hardware technology. Using a collection of real and synthetic filter sets, we prnvide analyses of DCFL performance and resource requirements on filter sets of various sizes and compositions. An optimized implementation of DCFL can provide over 100 million searchas per second and storage for over 200 thousand filters in a current generation FPGA or ASIC without the need for external memory devices.
The latest work on this category is the DCFL (Distributed Crossproducting of Field Labels) REF , which presents a combination of new and existing packet classification techniques that leverages observations of the structure of real filter sets and takes advantage of the capabilities of modern hardware technology.
Scalable packet classification using distributed crossproducing of field labels
Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies.
Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies.
Computer Science