Toward the Practical Use of Network Tomography for Internet Topology Discovery

Eriksson, Brian; Dasarathy, Gautam; Barford, Paul; Nowak, Robert

doi:10.1109/infcom.2010.5461970

Cited by 32 publications

(46 citation statements)

References 18 publications

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“…In the network tomography literature, recent work in [28], [29] has also examined how to decrease the number of probes required to reconstruct logical tree topologies using delay-based methods. In contrast to this prior work, our TargetComplete methodology focuses on the reduction of the number of source-destination pairs used to probe the network using standard off-the-shelf TTL limited probing techniques (e.g., traceroute), not on the crafting of special probes to minimize measurement load.…”

Section: Related Workmentioning

confidence: 99%

Inferring Unseen Components of the Internet Core

Eriksson

Barford

Sommers

et al. 2011

IEEE J. Select. Areas Commun.

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Despite many efforts over the past decade, the ability to generate topological maps of the Internet at the router-level accurately and in a timely fashion remains elusive. Mapping campaigns commonly involve traceroute-like probing that are usually non-adaptive and incomplete, thus revealing only a portion of the underlying topology. In this paper we demonstrate that standard probing methods yield datasets that implicitly contain information about much more than just the directly observed links and routers. Each probe yields information that places constraints on the underlying topology, and by integrating a large number of such constraints it is possible to accurately infer the existence of unseen components of the Internet (i.e., links and routers not directly revealed by the probing). Moreover, we show that this information can be used to adaptively re-focus the probing in order to more quickly discover the topology. These findings suggest radically new and more efficient approaches to Internet mapping. Our work focuses on the discovery of the core of the Internet. We define "Internet core" as the set of routers that is roughly bounded by ingress/egress routers from stub autonomous systems. We describe a novel data analysis methodology designed to accurately infer (i) the number of unseen core routers, (ii) the unseen hop-count distances between observed routers, and (iii) unseen links between observed routers. We use a large experimental dataset to validate the proposed methods. For our data set, we show that our methods can predict the number of unseen routers to within a 13% error level, estimate 60% of the unseen distances between observed routers to within a one-hop error, and robustly detect over 35% of the unseen links between observed routers. Furthermore, we use the information extracted by our inference methodology to drive an adaptive active-probing scheme. The adaptive probing method allows us to generate maps on our data set using 50% fewer probes than standard non-adaptive approaches.

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Section: Related Workmentioning

confidence: 99%

Inferring Unseen Components of the Internet Core

Eriksson

Barford

Sommers

et al. 2011

IEEE J. Select. Areas Commun.

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“…One body of work developed techniques for inferring 1-by-N (i.e., single-source tree) topologies using endto-end measurements [7][8][9][10][11][12][13][14][15]. Follow-up work [1][2][3] showed that an M -by-N topology can be decomposed into and reconstructed from a number of two-source, two-receiver (2-by-2) subnetwork components or "quartets".…”

mentioning

confidence: 99%

“…1 More specifically, we start from the problem of 2-by-N topology inference, which is an important special case and can then be used as a building block for inferring an Mby-N . Consistently with [1], we assume that a (static) 1-by-N topology is known (e.g., using one of the methods in [4,[7][8][9][10][11][12][13][14][15]23]). Then we query the quartet component by sending end-to-end probes between the two sources and the two receivers, and we learn its topology using some of the methods in [1,2,16,17,[24][25][26][27][28] 2 .…”

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confidence: 99%

Active Learning of Multiple Source Multiple Destination Topologies

Sattari

Kurant

Anandkumar

et al. 2014

IEEE Trans. Signal Process.

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Abstract-We consider the problem of inferring the topology of a network with M sources and N receivers (hereafter referred to as an M -by-N network), by sending probes between the sources and receivers. Prior work has shown that this problem can be decomposed into two parts: first, infer smaller subnetwork components (i.e., 1-by-N 's or 2-by-2's) and then merge these components to identify the M -by-N topology. In this paper, we focus on the second part, which had previously received less attention in the literature. In particular, we assume that a 1-by-N topology is given and that all 2-by-2 components can be queried and learned using end-to-end probes. The problem is which 2-by-2's to query and how to merge them with the given 1-by-N , so as to exactly identify the 2-by-N topology, and optimize a number of performance metrics, including the number of queries (which directly translates into measurement bandwidth), time complexity, and memory usage. We provide a lower bound, ⌈ N 2 ⌉, on the number of 2-by-2's required by any active learning algorithm and propose two greedy algorithms. The first algorithm follows the framework of multiple hypothesis testing, in particular Generalized Binary Search (GBS), since our problem is one of active learning, from 2-by-2 queries. The second algorithm is called the Receiver Elimination Algorithm (REA) and follows a bottom-up approach: at every step, it selects two receivers, queries the corresponding 2-by-2, and merges it with the given 1-by-N ; it requires exactly N − 1 steps, which is much less than all N 2 possible 2-by-2's. Simulation results over synthetic and realistic topologies demonstrate that both algorithms correctly identify the 2-by-N topology and are nearoptimal, but REA is more efficient in practice.

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“…Network tomography in [16], [17] and [18] can be applied for discovering the network topology on the Internet, which is generally referred to as network topology identication. On the Internet, the network topology is hidden from us since complex heterogeneous domains.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Furthermore, Eriksson et al [18] advocate the practical use of tomographic inference for accurate router-level topology discovery. The authors developed a Depth-First Search (DFS) ordering algorithm that clusters end host targets based on shared infrastructure.…”

Section: Literature Reviewmentioning

confidence: 99%

Network tomography application in mobile ad-hoc networks.

Khan¹

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Toward the Practical Use of Network Tomography for Internet Topology Discovery

Cited by 32 publications

References 18 publications

Inferring Unseen Components of the Internet Core

Inferring Unseen Components of the Internet Core

Active Learning of Multiple Source Multiple Destination Topologies

Network tomography application in mobile ad-hoc networks.

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