An Efficient Topology Discovery Based on Graph Partitioning for Software Defined Networks

This paper addresses the issues of low efficiency in topology discovery and link state information collection, as well as the high overhead of control messages in software defined networks. To address these issues, an efficient collection algorithm based on graph partitioning is proposed. The algorithm uses graph partitioning techniques to divide large-scale networks into multiple subgraphs. Information collection is then performed in parallel within each subgraph. This approach significantly improves collection efficiency. The proposed algorithm is validated through theoretical analysis and simulation experiments. The results show that the algorithm effectively reduces control message overhead.