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INFOCOM2023顶会

FlowBench: A Flexible Flow Table Benchmark for Comprehensive Algorithm Evaluation

Zhikang Chen, Ying Wan, Ting Zhang, Haoyu Song, Bin Liu

2023年份
2被引次数

摘要

Flow table is a fundamental and critical component in network data plane. Numerous algorithms and architectures have been devised for efficient flow table construction, lookup, and update. The diversity of flow tables and the difficulty to acquire real data sets make it challenging to give a fair and confident evaluation to a design. In the past, researchers rely on ClassBench and its improvements to synthesize flow tables, which become inadequate for today’s networks. In this paper, we present a new flow table benchmark tool, FlowBench. Based on a novel design methodology, FlowBench can generate large-scale flow tables with arbitrary combination of matching types and fields in a short time, and yet keep accurate characteristics to reveal the real performance of the algorithms under evaluation. The open-source tool facilitates researchers to evaluate both existing and future algorithms with unprecedented flexibility.

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