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Bipartite internet topology at the subnet-level
- in IEEE International Workshop on Network Science (NSW 2013), IEEE. West Point, NT: IEEE
, 2013
"... Abstract—Internet topology modeling involves capturing crucial characteristics of the Internet in producing synthetic network graphs. Selection of vital metrics is limited by our understanding of the Internet topology, which relies on the state of the art measurement studies. Recent measurement stud ..."
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Abstract—Internet topology modeling involves capturing crucial characteristics of the Internet in producing synthetic network graphs. Selection of vital metrics is limited by our understanding of the Internet topology, which relies on the state of the art measurement studies. Recent measurement studies indicate that underlying subnetworks, multi-access links providing one-hop connectivity to multiple nodes, are an important factor shaping the topology of the Internet. Current network models utilize point-to-point edges that can connect exactly two vertices of the topology. Decomposition of the underlying multi-access links into pairwise point-to-point edges results in cliques and is an oversimplification of the analyzed networks. Accurate modeling of multi-access links require special type of edges, i.e. hyper-edges, that can connect multiple vertices in a hyper-graph. Hyper-graphs are best illustrated as bipartite graphs where hyper-edges connect two types of vertices, i.e., router interfaces and subnets. In this paper, we introduce a bipartite model of the Internet topology and discuss representative synthetic network generation.
Internet topology discovery
- asurvey.IEEE Communications Surveys and Tutorials
, 2007
"... Abstract. Since the nineties, the Internet has seen an impressive growth, in terms of users, intermediate systems (such as routers), autonomous systems, or applications. In parallel to this growth, the research commu-nity has been looking for obtaining and modeling the Internet topology, i.e., how t ..."
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Abstract. Since the nineties, the Internet has seen an impressive growth, in terms of users, intermediate systems (such as routers), autonomous systems, or applications. In parallel to this growth, the research commu-nity has been looking for obtaining and modeling the Internet topology, i.e., how the various elements of the network interconnect between them-selves. An impressive amount of work has been done regarding how to collect data and how to analyse and model it. This chapter reviews main approaches for gathering Internet topology data. We first focus on hop limited probing, i.e., traceroute-like prob-ing. We review large-scale tracerouting projects and discuss traceroute limitations and how they are mitigated by new techniques or extensions. Hop limited probing can reveal an IP interface vision of the Internet. We next focus on techniques for aggregating several IP interfaces of a given router into a single identifier. This leads to a router level vision of the topology. The aggregation can be done through a process called alias resolution. We also review a technique based on IGMP probing that silently collect all multicast interfaces of a router into a single probe. We next refine the router level topology by adding subnet information. We finish this chapter by discussing the AS level topology, in particular the relationships between ASes and the induced hierarchy.
Estimating Network Layer Subnet Characteristics via Statistical Sampling
"... Abstract. Network layer Internet topology consists of a set of routers connected to each other through subnets. Recently, there has been a significant interest in studying topological characteristics of subnets in addition to routers in the Internet. However, given the size of the Internet, construc ..."
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Abstract. Network layer Internet topology consists of a set of routers connected to each other through subnets. Recently, there has been a significant interest in studying topological characteristics of subnets in addition to routers in the Internet. However, given the size of the Internet, constructing complete subnet level topology maps is neither practical nor economical. A viable solution, then, is to sample subnets in the target domain and estimate their global characteristics. In this study, we propose a sampling framework for subnets; derive proper estimators for various subnet characteristics including total number of subnets, subnet prefix length distribution, mean subnet degree, and IP address utilization; and analyze the theoretical and empirical aspects of these estimators.
Impact of Sampling Design in Estimation of Graph Characteristics
"... Abstract—Studying structural and functional characteristics of large scale graphs (or networks) has been a challenging task due to the related computational overhead. Hence, most studies consult to sampling to gather necessary information to estimate various features of these big networks. On the ot ..."
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Abstract—Studying structural and functional characteristics of large scale graphs (or networks) has been a challenging task due to the related computational overhead. Hence, most studies consult to sampling to gather necessary information to estimate various features of these big networks. On the other hand, using a best effort approach to graph sampling within the constraints of an application domain may not always produce accurate estimates. In fact, the mismatch between the characteristics of interest and the utilized network sampling methodology may result in incorrect inferences about the studied characteristics of the underlying system. In this study we empirically investigate the sources of information loss in a sampling process; identify the fundamental factors that need to be carefully considered in a sampling design; and use several synthetic and real world graphs to elaborately demonstrate the mismatch between the sampling design and graph characteristics of interest. I.
Cross-Check of Analysis Modules
, 2015
"... This deliverable presents an extended set of Analysis Modules, including both the improvements done to those presented in deliverable D4.1 as well as the new analysis algorithms designed and developed to address use-cases. The deliverable also describes a complete workflow descripƟon for the differe ..."
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This deliverable presents an extended set of Analysis Modules, including both the improvements done to those presented in deliverable D4.1 as well as the new analysis algorithms designed and developed to address use-cases. The deliverable also describes a complete workflow descripƟon for the different use-cases, including both stream processing for real-Ɵme monitoring applicaƟons as well as batch processing for “off-line ” analysis. This workflow descripƟon specifies the iteraƟve interacƟon loop between WP2, WP3, T4.1, and T4.2, thereby allowing for a cross-checking of the analysis modules and the reasoner interacƟons.
