FlowBench: A Flexible Flow Table Benchmark for Comprehensive Algorithm Evaluation
Zhikang Chen, Ying Wan, Ting Zhang, Haoyu Song, Bin Liu
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.
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。
相关 Paper
- Flow Algebra: Towards an Efficient, Unifying Framework for Network Management TasksChristopher Leet, Robert Soulé, Yang Richard Yang, Ying ZhangINFOCOM 2021 · 被引用 3 次
- Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS BenchmarksArber Zela, Julien Niklas Siems, Lucas Zimmer, Jovita Lukasik 等ICLR 2022 · 被引用 100 次
- LakeBench: A Benchmark for Discovering Joinable and Unionable Tables in Data LakesYuhao Deng, Chengliang Chai, Lei Cao, Qin Yuan 等VLDB 2024 · 被引用 36 次
- NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly EasyYash Mehta, Colin White, Arber Zela, Arjun Krishnakumar 等ICLR 2022 · 被引用 54 次
- TAOBench: An End-to-End Benchmark for Social Networking WorkloadsAudrey Cheng, Xiao Shi, Aaron N. Kabcenell, Shilpa Lawande 等VLDB 2022 · 被引用 20 次
