NeuralMon: Graph Neural Network for Flow Measurement Allocation
Yang Wang, Xiong Wang, Zhuobin Huang, Ci He, Yasheng Zhang, Shizhong Xu · 2021 IEEE Global Communications Conference (GLOBECOM) · 2021
Fine-grained and accurate network flow measurements are essential for various network management tasks. In recent years, the evolution of programmable networks enables flow measurement on the switch. However, limited hardware resources on programmable switches drive the shift of measurement from a single switch to network-wide coordinations. This paper aims to optimize the allocation strategy of flow measurement among switches under the objective of measurement coverage and accuracy in network-wide measurement scenarios. We design a Graph Neural Network model, NeuralMon, that can model and solve the above problem precisely. NeuralMon converts network topologies and network flows into a hypergraph and transforms the flow measurement task allocation problem into a node classification problem. NeuralMon is effective in learning the task allocation solution from the network topologies and flows directly. Even on untrained real-world network topologies, NeuralMon still provides excellent performance.