An intelligent optimization-based traffic information acquirement approach to software-defined networking

Internet of things (IoT) is a global information infrastructure that supports access to thousands of monitoring devices and user terminals. A large amount of monitoring data generated by IoT is integrated to cloud computing through the network to improve the quality of life of citizens. Fine-grained and accurate traffic information is important for IoT network management. Software-defined networking (SDN) is a centralized control plane as a logical control center, making network management more flexible and efficient. Then, we collect fine-grained traffic information in SDN-based IoT networks to improve network management. To acquire the traffic information with low overhead and high accuracy, first, we collect the statistics of coarse-grained traffic of flows and fine-grained traffic of links, and then we utilize the intelligent optimization methods to estimate the network traffic. To improve the granularity and accuracy of the acquired traffic information, we construct an optimization function with constraints to decrease the estimation errors. As the optimization function of traffic information is a non-deterministic polynomial-hard problem, we present a heuristic algorithm to obtain the optimal solution of the fine-grained measurement. Finally, we conduct some simulations to verify the proposed measurement scheme. Simulation results show that our approach can improve the granularity and accuracy of traffic information with intelligent optimization methods.