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pro vyhledávání: '"Bütün, Beyza"'
Recent endeavours have enabled the integrationof trained machine learning models like Random Forests inresource-constrained programmable switches for line rate inference.In this work, we first show how packet-level informationcan be used to classify
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e607e4d5bcee16272ad9cb20089116f
Existing approaches for in-switch inference with Random Forest (RF) models that can run on production-level hardware do not support flow-level features and have limited scalability to the task size. This leads to performance barriers when tackling co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_____10188::2015e2fc97bae3ac4220881dd56ce61d
https://hdl.handle.net/20.500.12761/1678
https://hdl.handle.net/20.500.12761/1678
The recent proliferation of programmable network equipment has opened up new possibilities for embedding intelligence into the data plane. Deploying models directly in the data plane promises to achieve high throughput and low latency inference capab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::771b53cb78532e665393698c931a40c1