Abstract:
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 s...Show MoreMetadata
Abstract:
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 complex inference problems with sizable decision spaces. Flowrest is a complete RF model framework that fills existing gaps in the existing literature and enables practical flow-level inference in commercial programmable switches. In this demonstration, we exhibit how Flowrest can classify individual traffic flows at line rate in an experimental platform based on Intel Tofino switches. To this end, we run experiments with real-world measurement data, and show how Flowrest yields improvements in accuracy with respect to solutions that are limited to packet-level inference in programmable hardware.
Published in: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Date of Conference: 20-20 May 2023
Date Added to IEEE Xplore: 29 August 2023
ISBN Information: