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Efficient In-Network Traffic Classification Using Programmable Switches With AdaFlow
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DOI：10.1109/TNSM.2025.3607406.png)
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Abstract
En 中文
In-network ML-based traffic classification using programmable switches has enabled faster decisions and reduced the cost of the security infrastructure and management overheads. However, due to constraints on per-packet operations and limited stateful memory in the switch data plane, there is a fundamental tradeoff between traffic classification accuracy and switch memory requirements. Existing works fall short of accurately classifying traffic with diverse flow characteristics while keeping the memory footprint low. In this paper, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AdaFlow</monospace>, a system that aims to address this gap by incorporating traffic-specific heuristics while designing the in-network classifier. We evaluate the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AdaFlow</monospace> prototype via simulations and also on a testbed with an Intel Barefoot Tofino switch. Compared to the state-of-the-art, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AdaFlow</monospace> improves accuracy up to 7% for various use-cases while keeping the memory overheads similar to or lower than those of the existing systems.
Keywords:
Computer networks
data plane programmability
machine learning
network security
programmable switches
software defined networking
traffic classification
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