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On the Importance of Local Connectivity for Internet Topology Models
 In 21st International Teletraffic Congress (ITC
"... (AS) topology generation make structural assumptions about the AS graph. Those assumptions typically stem from beliefs about the true properties of the Internet, e.g. hierarchy and powerlaws, which arise from incorrect interpretations of incomplete observations of the AS topology. In this paper we c ..."
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Cited by 7 (1 self)
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(AS) topology generation make structural assumptions about the AS graph. Those assumptions typically stem from beliefs about the true properties of the Internet, e.g. hierarchy and powerlaws, which arise from incorrect interpretations of incomplete observations of the AS topology. In this paper we compare AS topology generation models with several observed AS topologies without making assumptions as to the relative importance of different topological characteristics. We find that although existing AS topology models capture degreebased properties well, they fail to capture the complexity of the local interconnection structure between ASes. We use a wide range of metrics including the weighted spectral distribution and make it available as toolbox 1. We show that the shortcomings of existing models stem from underestimating the complexity of connectivity in the core due to incomplete understanding of collected data limitations, and narrow focus on particular aspects of the AS topology structure. I.
Weighted spectral distribution for internet topology analysis: Theory and applications
 IEEE/ACM TRANSACTIONS ON NETWORKING
"... Comparing graphs to determine the level of underlying structural similarity between them is a widely encountered problem in computer science. It is particularly relevant to the study of Internet topologies, such as the generation of synthetic topologies to represent the Internet’s AS topology. We de ..."
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Cited by 5 (3 self)
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Comparing graphs to determine the level of underlying structural similarity between them is a widely encountered problem in computer science. It is particularly relevant to the study of Internet topologies, such as the generation of synthetic topologies to represent the Internet’s AS topology. We derive a new metric that enables exactly such a structural comparison, the weighted spectral distribution. We then apply this metric to three aspects of the study of the Internet’s AS topology. (i) we use it to quantify the effect of changing the mixing properties of a simple synthetic network generator. (ii) we use this quantitative understanding to examine the evolution of the Internet’s AS topology over approximately 7 years, finding that the distinction between the Internet core and periphery has blurred over time. (iii) we use the metric to derive optimal parameterizations of several widely used AS topology generators with respect to a largescale measurement of the real AS topology.
The Forwarding on Gates Architecture: Merging IntServ and DiffServ
"... Abstract—Quality of Service (QoS) will be a major enabler for Future Internet applications and services. However, today’s Internet provides no suitable QoS support for endtoend connections due to several drawbacks of IntServ and DiffServ. Therefore, this paper proposes the “Forwarding on Gates” ar ..."
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Abstract—Quality of Service (QoS) will be a major enabler for Future Internet applications and services. However, today’s Internet provides no suitable QoS support for endtoend connections due to several drawbacks of IntServ and DiffServ. Therefore, this paper proposes the “Forwarding on Gates” architecture, which uses a new network protocol designed to handle IntServ and DiffServ in an integrated way. The architecture supports resource reservations for QoS guarantees, like in IntServ scenarios, and prioritized traffic, like in DiffServ scenarios. Furthermore, a combination of both is supported. This paper introduces the architecture and defines the network protocol used to implement these features. The evaluation includes theoretical descriptions of the network configuration for the different scenarios and simulation results concerning the protocol overhead in largescale networks. Our new architecture is able to support QoS in a scalable way, since it allows a network providing QoS to move states and delegate decisions about the QoS usage to the entity using the QoS. Keywords—Future Internet; network protocol; architecture; QoS.
Analysis of the Internet’s Structural Evolution
, 2009
"... In this paper we study the structural evolution of the AS topology as inferred from two different datasets over a period of seven years. We use a variety of topological metrics to analyze the structural differences revealed in the AS topologies inferred from the two different datasets. In particular ..."
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In this paper we study the structural evolution of the AS topology as inferred from two different datasets over a period of seven years. We use a variety of topological metrics to analyze the structural differences revealed in the AS topologies inferred from the two different datasets. In particular, to focus on the evolution of the relationship between the core and the periphery, we make use of the weighted spectral distribution. We find that the traceroute dataset has increasing difficulty in sampling the periphery of the AS topology, largely due to limitations inherent to active probing. Such a dataset has too limited a view to properly observe topological changes at the ASlevel compared to a dataset largely based on BGP data. We also highlight limitations in current measurements that require a better sampling of particular topological properties of the Internet. Our results indicate that the Internet is changing from a corecentered, strongly customerprovider oriented, disassortative network, to a softhierarchical, peeringoriented, assortative network.
Categories and Subject Descriptors
, 807
"... Many models have been proposed to generate Internet Autonomous System (AS) topologies, most of which make structural assumptions about the AS graph. In this paper we compare AS topology generation models with several observed AS topologies. In contrast to most previous works, we avoid making assumpt ..."
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Many models have been proposed to generate Internet Autonomous System (AS) topologies, most of which make structural assumptions about the AS graph. In this paper we compare AS topology generation models with several observed AS topologies. In contrast to most previous works, we avoid making assumptions about which topological properties are important to characterize the AS topology. Our analysis shows that, although matching degreebased properties, the existing AS topology generation models fail to capture the complexity of the local interconnection structure between ASs. Furthermore, we use BGP data from multiple vantage points to show that additional measurement locations significantly affect local structure properties, such as clustering and node centrality. Degreebased properties, however, are not notably affected by additional measurements locations. These observations are particularly valid in the core. The shortcomings of AS topology generation models stems from an underestimation of the complexity of the connectivity in the core caused by inappropriate use of BGP data.
Network clustering via spectral projections
"... This paper proposes a novel nonparametric technique for clustering networks based on their structure. Many topological measures have been introduced in the literature to characterize topological properties of networks. These measures provide meaningful information about the structural properties of ..."
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This paper proposes a novel nonparametric technique for clustering networks based on their structure. Many topological measures have been introduced in the literature to characterize topological properties of networks. These measures provide meaningful information about the structural properties of a network, but many networks share similar values of a given measure [1]. Furthermore, strong correlation between these measures occur on realworld graphs [2], so that using them to distinguish arbitrary graphs is difficult in practice [3]. Although a very complicated way to represent the information and the structural properties of a graph, the graph spectrum [4] is believed to be a signature of a graph [5]. A weighted form of the distribution of the graph spectrum, called the weighted spectral distribution (WSD), is proposed here as a feature vector. This feature vector may be related to actual structure in a graph and in addition may be used to form a metric between graphs; thus ideal for clustering purposes. To distinguish graphs, we propose to rely on two ways to project a weighted form of the eigenvalues of a graph into a lowdimensional space. The lower dimensional projection, turns out to nicely distinguish different classes of graphs, e.g. graphs from network topology generators [6, 7, 8], Internet application graphs [9], and dKrandom graphs [10]. This technique can be used advantageously to separate graphs that would otherwise require complex sets of topological measures to be distinguished [9].
WEIGHTED SPECTRAL DISTRIBUTION: A METRIC FOR STRUCTURAL ANALYSIS OF NETWORKS
"... We consider the problem of structural comparison of graphs with a focus on a particular dynamic graph, the Internet’s Autonomous System (AS) topology (§1.2). We develop the weighted spectral distribution (WSD), a metric based on the distribution of a particular decomposition of a graph’s structure ( ..."
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We consider the problem of structural comparison of graphs with a focus on a particular dynamic graph, the Internet’s Autonomous System (AS) topology (§1.2). We develop the weighted spectral distribution (WSD), a metric based on the distribution of a particular decomposition of a graph’s structure (§1.3) with a worked example (§1.4). We then turn to our particular application domain (§1.5), describing existing measures used to characterize Internet topologies, common topology generators, and several observed datasets used in our evaluation. We then compare the topology generators to the observed datasets using both existing measures and the WSD (§1.6), use the WSD to examine the impact of varying parameter selection for the different generators (§1.7), and optimize parameter values for the generators with respect to one of the observed datasets and examine the results using both WSD and traditional measures (§1.8). Finally we look briefly, from a particular vantage point, at the structural evolution of the Internet topology (§1.9), before concluding (§1.10).
services is Quality of Service (QoS). Current Internet
"... technologies provide no suitable QoS support for endtoend connections due to several drawbacks of IntServ and DiffServ. In this article, we propose the “Forwarding on Gates ” (FoG) architecture, which answers the QoS questions by the help of a new internetwork architecture. It applies its own new ..."
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technologies provide no suitable QoS support for endtoend connections due to several drawbacks of IntServ and DiffServ. In this article, we propose the “Forwarding on Gates ” (FoG) architecture, which answers the QoS questions by the help of a new internetwork architecture. It applies its own new network protocol, which was designed to handle IntServ and DiffServ in an integrated way. FoG supports resource reservations for QoS guarantees in IntServ scenarios and prioritized traffic in DiffServ scenarios as well as a combination of both. As core advantage, the QoS support of FoG works in a scalable way by allowing a network to move QoS states and delegate decisions about the QoS usage to the entities demanding for QoS. This article describes the architecture, its network protocol, and solutions for interoperability with current networks. The evaluation includes theoretical descriptions of network configurations for a use case not supported by IP. Moreover, simulations show that the protocol overhead is comparable to IPv6, although packets can select QoS explicitly. Measured routing graph sizes for various setups show the flexibility of the FoG architecture. Keywords—Future Internet; network protocol; architecture;