Generation of 2-mode scale-free graphs for link-level internet topology modeling

Abstract

Comprehensive analysis that aims to understand the topology of real-world networks and the development of algorithms that replicate their characteristics has been significant research issues. Although the accuracy of newly developed network protocols or algorithms does not depend on the underlying topology, the performance generally depends on the topology. As a result, network practitioners have concentrated on generating representative synthetic topologies and utilize them to investigate the performance of their design in simulation or emulation environments. Network generators typically represent the Internet topology as a graph composed of point-to-point links. In this study, we discuss the implications of multi-access links on the synthetic network generation and modeling of the networks as bi-partite graphs to represent both subnetworks and routers. We then analyze the characteristics of sampled Internet topology data sets from backbone Autonomous Systems (AS) and observe that in addition to the commonly recognized power-law node degree distribution, the subnetwork size and the router interface distributions often exhibit power-law characteristics. We introduce a SubNetwork Generator (SubNetG) topology generation approach that incorporates the observed measurements to produce bipartite network topologies. In particular, generated topologies capture the 2-mode relation between the layer-2 (i.e., the subnetwork and interface distributions) and the layer-3 (i.e., the degree distribution) that is missing from the current network generators that produce 1-mode graphs. The SubNetG source code and experimental data is available at https://github.com/netml/sonet.

Analytical Subnetwork and IP Alias Resolution for Network Tomography Using Path Traces

“…Internet topology measurements obtain topologies with various density of path traces, and hence, a single parameter is not suitable in all cases. In order to assure the most optimal parameters to be used with every network that ASIAR is run on, we use evolutionary computing to automatically detect the best parameters on a synthetic network that mimics the input network generated by SONET [50,51].…”

“…We obtain synthetic networks that contain subnetworks and routers with multiple interfaces using the Subnetwork Oriented NETwork (SONET) topology generator [50,51]. The IP distribution for the 1000 and 10,000 node networks are provided in Figure 5 using a Hilbert curve plot.…”

“…We utilized the SONET topology generator to obtain synthetic networks with powerlaw distributions for the subnetworks and IP aliases [50,51]. We generated five networks, each containing 1000 routers, which included around 1900 IP addresses and 600 subnets.…”