Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Gucciardo, Michele"'
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
Autor:
Chatzieleftheriou, Livia Elena, Gramaglia, Marco, Camelo, Miguel, Garcia-Saavedra, Andres, Kosmatos, Evangelos, Gucciardo, Michele, Soto, Paola, Iosifidis, George, Fuentes, Lidia, García-Aviles, Ginés, Lutu, Andra, Baldoni, Gabriele, Fiore, Marco
The quest for autonomous mobile networks introduces the need for fully native support for Network Intelligence (NI) algorithms, typically based on Artificial Intelligence tools like Machine Learning, which shall be gathered into a NI stratum. The NI
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c4d755c98a50feb8b3d45ffea9a0b1e
User-plane machine learning facilitates low-latency, high-throughput inference at line rate. Yet, user planes are highly constrained environments, and restrictions are especially marked in programmable switches with limited memory and minimum support
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::290d6c527354746ede2cfc67d50605c8
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
Autor:
Nogueras, Antonio Bazco, Fiore, Marco, Avilés, Ginés García, Gucciardo, Michele, Camelo, Miguel, Soto, Paola, Chang, Chia-Yu, De Vleeschauwer, Danny, Lozano, Josep Xavier Salvat, Saavedra, Andrés García, Li, Xi, Kostopoulos, Alexandros, Lutu, Andra, Segura, Carlos, Gramaglia, Marco, Ballesteros, Joaquin, Fuentes, Lidia, Kosmatos, Evangelos, Paez, Ivan
This is the second public deliverable of WP2 of the DAEMON project. It builds upon the material of the previous deliverable of WP2, i.e., D2.1, and on activities and results achieved during the first iteration of the project in WP3, WP4 and WP5. As a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8763c1f204db8b08470aef763274616
We comparatively evaluate state-of-the-art solutions for in-switch machine learning inference. We demonstrate that random forest (RF) models attain accuracies on par with those of approaches based on neural networks, which are also less amenable to i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_____10188::63d869309391f91be7ee03ee7800af10
https://hdl.handle.net/20.500.12761/1692
https://hdl.handle.net/20.500.12761/1692
Autor:
Gramaglia, Marco, Naram Mhaisen, Garcia-Saavedra, Andres, Garcia, Gines, Chang, Chia-Yu, De Vleeschauwer, Danny, Molina, Maria, Slamnik-Krijestorac, Nina, Soto, Paola, Camelo, Miguel, Fuentes, Lidia, Amor, Mercedes, Pinto, Monica, Ca��ete, ��ngel, Munoz, Daniel-Jesus, Fiore, Marco, Gucciardo, Michele, Lutu, Andra
This deliverable presents DAEMON���s initial view on the problem of real-time control and network functions intelligence. The work presented in this document tackles two main points: ��� Understanding what the main challenges from the arc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e291f6f1614440f98d8dbc322b54f44
Autor:
Croce, Daniele, Gucciardo, Michele, Santaromita, Giuseppe, Mangione, Stefano, Tinnirello, Ilenia
Publikováno v:
In LPWAN Technologies for IoT and M2M Applications 2020:181-197