A Profit-aware Adaptive Approach for In-Network Traffic Classification - Archive ouverte HAL
Communication Dans Un Congrès Année : 2023

A Profit-aware Adaptive Approach for In-Network Traffic Classification

Résumé

In-network traffic classification is a new paradigm in developing accurate and early-stage traffic classification solutions. However, despite having good accuracy, the one-fit machine learning model becomes outdated as the traffic pattern changes over time. This changing traffic pattern leads to misclassification, i.e., incorrect mapping of traffic flows to the Quality of Service (QoS) classes, resulting in a service quality violation and the imposition of a penalty. This paper proposes a profit-aware adaptive traffic classification approach in the data plane. We particularly design an economic model to measure the impact of per-class misclassification rate on the infrastructure provider's profit and use an adaptive method to handle misclassification directly inside a programmable data plane. The evaluation result shows that optimal path allocation for various traffic classes determines the targeted revenue, while improving classifier accuracy reduces penalty and maintains the maximum profit.
Fichier principal
Vignette du fichier
a549-saqib stamped.pdf (586.22 Ko) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04139681 , version 1 (27-06-2023)

Identifiants

  • HAL Id : hal-04139681 , version 1

Citer

Muhammad Saqib, Halima Elbiaze, Roch Glitho. A Profit-aware Adaptive Approach for In-Network Traffic Classification. IEEE International Conference on Communications, May 2023, Rome, Italy. ⟨hal-04139681⟩
17 Consultations
59 Téléchargements

Partager

More