Congestion versus accuracy tradeoffs in IP traffic classification

Real-time internet traffic classification has potential applications in next-generation internet security and bandwidth management. Current machine learning-based algorithms for traffic classification, however, present scalability issues that would degrade system performance if executed to make control decisions on real-time streams. This tension gives rise to competing performance costs for traffic classification systems: higher throughput can be achieved at the expense of less stringent computation, and thus lower accuracy. In this paper, we develop a queueing model to explicitly weigh the tradeoff between accuracy and congestion costs in binary classification tasks of discretionary duration. We show the optimal control policy can be approximated well using standard dynamic programming techniques, and compare its performance against two benchmark policies. We also propose a simple heuristic based on constructing conic hulls, and show its performance is very close to optimal.