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- W4384157890 abstract "Recent endeavours have enabled the integration of trained machine learning models like Random Forests in resource-constrained programmable switches for line rate inference. In this work, we first show how packet-level information can be used to classify individual packets in production-level hardware with very low latency. We then demonstrate how the newly proposed Flowrest framework improves classification performance relative to the packet-level approach by exploiting flow-level statistics to instead classify traffic flows entirely within the switch without considerably increasing latency. We conduct experiments using measurement data in a real-world testbed with an Intel Tofino switch and shed light on how Flowrest achieves an F1-score of 99% in a service classification use case, outperforming its packet-level counterpart by 8%." @default.
- W4384157890 created "2023-07-14" @default.
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- W4384157890 date "2023-06-19" @default.
- W4384157890 modified "2023-09-24" @default.
- W4384157890 title "Showcasing In-Switch Machine Learning Inference" @default.
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- W4384157890 doi "https://doi.org/10.1109/netsoft57336.2023.10175464" @default.
- W4384157890 hasPublicationYear "2023" @default.
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