1 research outputs found
A Computational Approach to Packet Classification
Multi-field packet classification is a crucial component in modern
software-defined data center networks. To achieve high throughput and low
latency, state-of-the-art algorithms strive to fit the rule lookup data
structures into on-die caches; however, they do not scale well with the number
of rules. We present a novel approach, NuevoMatch, which improves the memory
scaling of existing methods. A new data structure, Range Query Recursive Model
Index (RQ-RMI), is the key component that enables NuevoMatch to replace most of
the accesses to main memory with model inference computations. We describe an
efficient training algorithm that guarantees the correctness of the
RQ-RMI-based classification. The use of RQ-RMI allows the rules to be
compressed into model weights that fit into the hardware cache. Further, it
takes advantage of the growing support for fast neural network processing in
modern CPUs, such as wide vector instructions, achieving a rate of tens of
nanoseconds per lookup. Our evaluation using 500K multi-field rules from the
standard ClassBench benchmark shows a geometric mean compression factor of
4.9x, 8x, and 82x, and average performance improvement of 2.4x, 2.6x, and 1.6x
in throughput compared to CutSplit, NeuroCuts, and TupleMerge, all
state-of-the-art algorithms.Comment: To appear in SIGCOMM 202