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

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 However, despite having good accuracy, the one-fit machine learning model becomes outdated as the traffic pattern changes over time.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.