Optimizing Traffic Measurement Task Deployment in Programmable Networks

Traffic measurement is critical for network manage-ment. The programmable networking paradigm paves the way for implementing fine-grained and accurate traffic measurement. However, in programmable networking, the programmable re-sources on hardware switches are highly limited. Most existing solutions have not considered the deployment of multiple traffic measurement tasks under resource constraints in programmable networks. To address the issue, we construct the network model and problem formulation, and we refer to this problem as the traffic measurement task deployment problem and prove it is NP-hard. To solve it, we proposed an approximate algorithm called Ant Colony Optimization with Dynamic Pruning (ACO-DP). We conducted simulations on the Fat-Tree topologies to evaluate the performance of ACO-DP. The evaluation results show that ACO-DP can achieve much higher overall measurement utility and faster convergence compared to other benchmark algorithms, and the solutions returned by ACO-DP are very close to the optimal solutions.