Large-Scale Emulation Network Topology Partition Based on Community Detection With the Weight of Vertex Similarity

Abstract

Abstract Due to the limitations of physical resources, if a large-scale emulation network environment composed of millions of vertices and edges is constructed by virtualization technology, the whole network topology should be partitioned into a set of subnets. The topology partition is a work of graph partition. The existing topology partition methods have shortcomings, such as low efficiency and poor practicability, especially for large-scale network topology. The emulation network is a kind of complex network and has the characteristics of community structure. Therefore, we proposed LENTP (large-scale emulation network topology partition) based on the community detection with the weight of the vertex similarity for large-scale topology partition. In the first stage, the tree-structured area compression reduces the topology scales significantly to improve partition efficiency. And then, the improved Louvain algorithm is used to topology partitioning and obtain an initial set of subnets with the minimum number of subnets and remote links. Finally, after repartitioning and merging for the initial subnets, the result of subnets is the final topology partition that reaches the optimization objectives with the conditions of the virtual resources. In the experiment, the method is tested in five groups of network topology with different scales. The results demonstrate that LENTP can partition the network topology over 1 000 000 nodes and significantly improve the running-time efficiency of the network topology partition.