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Showcasing In-Switch Machine Learning Inference

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Netsoft_2023_Demo_author_version.pdf (715.5Kb)
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URI: https://hdl.handle.net/20.500.12761/1693
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Autor(es)
Akem, Aristide Tanyi-Jong; Bütün, Beyza; Gucciardo, Michele; Fiore, Marco
Fecha
2023-06-19
Resumen
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%.
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Ficheros
Netsoft_2023_Demo_author_version.pdf (715.5Kb)
Identificadores
URI: https://hdl.handle.net/20.500.12761/1693
Metadatos
Mostrar el registro completo del ítem

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