| Full text | |
| Author(s): |
Xavier, Bruno Missi
;
Silva Guimaraes, Rafael
;
Comarela, Giovanni
;
Martinello, Magnos
Total Authors: 4
|
| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT; v. 19, n. 4, p. 13-pg., 2022-12-01. |
| Abstract | |
Self-driving networks guided by machine-learning (ML) algorithms are the driving force for building networks of the future. ML is effective at making inferences about data that is too complex or too unpredictable for humans. The network softwarization enabled by a deep programmability approach opens up new opportunities to deploy ML at the programmable data plane. In this paper, we introduce the MAP4 as a framework that explores the feasibility of mapping ML models in programmable network devices. To achieve this, we rely on the P4 language to deploy a pre-trained model into a programmable switch, utilizing the ML model to accurately classify flows at line rate. Our approach demonstrates that ML models working as classifiers can better fit the data by using the new levels of network programmability from the P4 language. The results showed that with few packets, most of the flows are properly classified. In some use cases, with two packets in the flow, 97% of traffic can be correctly classified, and all classes are properly labeled with a maximum of four packets. (AU) | |
| FAPESP's process: | 20/05182-3 - PORVIR-5G: programability, orchestration and virtualization in 5G networks |
| Grantee: | José Marcos Silva Nogueira |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 20/05174-0 - SAWI - Savvy Access through Worldwide Internet |
| Grantee: | Epaminondas Aguiar de Sousa Junior |
| Support Opportunities: | Research Grants - Innovative Research in Small Business - PIPE |
| FAPESP's process: | 18/23097-3 - SFI2: slicing future internet infrastructures |
| Grantee: | Tereza Cristina Melo de Brito Carvalho |
| Support Opportunities: | Research Projects - Thematic Grants |