Performance modeling and optimizations for decomposition-based large-scale packet classification on multi-core processors

Abstract

Abstract-Large-scale packet classification such as OpenFlow table lookup in Software Defined Networking (SDN) is a key task performed at the Internet routers. However, the increasing size of the rule set and the increasing width of each individual rule make large-scale packet classification a challenging problem. In this paper, we present a decompositionbased approach for large-scale packet classification on multicore processors. We develop a model to predict the performance of the classification engine with respect to throughput and latency. This model involves the architectural parameters of the multi-core processors and the design requirements of packet classification. Based on this model, we employ optimization techniques such as grouping short fields in the search phase and early termination of the merge phase. The performance model can be applied to other generic multi-field classification problems as well. To evaluate the accuracy of the performance model, we implement a 15-field classification engine on stateof-the-art multi-core processors. Experimental results show that, the proposed model predicts the performance with less than ±10% error. For a 32 K 15-field rule set, the optimized decomposition-based approach achieves 2000 ns per packet latency and 33 Million Packets Per Second (MPPS) throughput (49% of the peak throughput). The peak performance assumes an ideal execution model that uses an optimized execution sequence and ignores memory access latency, data dependencies, and context switch overhead.

An Efficient Parallel Algorithm for Detecting Packet Filter Conflicts

“…In packet classification applications, the classification process for each packet is independent. Due to the development trend of CPUs, researchers have investigated packet classification on multicore CPUs [8][9][10][11][12]. However, from the viewpoint of parallelism, the number of cores is not sufficiently high, and the scale of performance improvement is limited.…”