Caravan: Practical Online Learning of In-Network ML Models with Labeling Agents
Recent work on in-network machine learning (ML) anticipates offline models to operate well in modern networking environments. However, upon deployment, these models struggle to cope with fluctuating traffic patterns and network conditions and, therefore, must be validated and updated frequently in an online fashion.
Authors
- Nathan Zhang (ORCID: https://orcid.org/0000-0002-9668-902X)
- Kunle Olukotun (ORCID: https://orcid.org/0000-0002-8779-0636)
- Enkeleda Bardhi (ORCID: https://orcid.org/0000-0002-3099-5374)
- Tushar Swamy (ORCID: https://orcid.org/0000-0002-2975-6042)
- Muhammad Shahbaz (ORCID: https://orcid.org/0000-0001-5168-9045)
- Qizheng Zhang (ORCID: https://orcid.org/0009-0009-3208-4601)
- Ali Imran (ORCID: https://orcid.org/0009-0009-7194-2357)
Institutions
- Purdue University West Lafayette (US)
- University of Michigan (US)
- Sapienza University of Rome (IT)
- Stanford University (US)
Publication Details
- Published
- 2024-11-04
- DOI
- https://doi.org/10.1145/3704742.3704964
- Citations
- 7
- Primary Topic
- Data Stream Mining Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.43