111 research outputs found
Active Topology Inference using Network Coding
Our goal is to infer the topology of a network when (i) we can send probes
between sources and receivers at the edge of the network and (ii) intermediate
nodes can perform simple network coding operations, i.e., additions. Our key
intuition is that network coding introduces topology-dependent correlation in
the observations at the receivers, which can be exploited to infer the
topology. For undirected tree topologies, we design hierarchical clustering
algorithms, building on our prior work. For directed acyclic graphs (DAGs),
first we decompose the topology into a number of two-source, two-receiver
(2-by-2) subnetwork components and then we merge these components to
reconstruct the topology. Our approach for DAGs builds on prior work on
tomography, and improves upon it by employing network coding to accurately
distinguish among all different 2-by-2 components. We evaluate our algorithms
through simulation of a number of realistic topologies and compare them to
active tomographic techniques without network coding. We also make connections
between our approach and alternatives, including passive inference, traceroute,
and packet marking
Active Learning of Multiple Source Multiple Destination Topologies
We consider the problem of inferring the topology of a network with
sources and receivers (hereafter referred to as an -by- 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., -by-'s or -by-'s) and then merge these components
to identify the -by- 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 -by- topology is given and that all
-by- components can be queried and learned using end-to-end probes. The
problem is which -by-'s to query and how to merge them with the given
-by-, so as to exactly identify the -by- 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, , on the number of
-by-'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 -by- 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 -by-, and
merges it with the given -by-; it requires exactly steps, which is
much less than all possible -by-'s. Simulation results
over synthetic and realistic topologies demonstrate that both algorithms
correctly identify the -by- topology and are near-optimal, but REA is
more efficient in practice
Analysis on binary loss tree classification with hop count for multicast topology discovery
Copyright © 2004 IEEEThe use of multicast inference on end-to-end measurement has recently been proposed as a means of obtaining the underlying multicast topology. We analyze the algorithm of binary loss tree classification with hop count (HBLT). We compare it with the binary loss tree classification algorithm (BLT) and show that the probability of misclassification of HBLT decreases more quickly than that of BLT as the number of probing packets increases. The inference accuracy of HBLT is always 1 (the inferred tree is identical to the physical tree) in the case of correct classification, whereas that of BLT is dependent on the shape of the physical tree and inversely proportional to the number of internal nodes with a single child. Our analytical result shows that HBLT is superior to BLT, not only on time complexity, but also on misclassification probability and inference accuracy.Hui Tian, Hong She
Multicast-based Weight Inference in General Network Topologies
Network topology plays an important role in many
network operations. However, it is very difficult to obtain
the topology of public networks due to the lack of internal
cooperation. Network tomography provides a powerful solution
that can infer the network routing topology from end-to-end
measurements. Existing solutions all assume that routes from a
single source form a tree. However, with the rapid deployment
of Software Defined Networking (SDN) and Network Function
Virtualization (NFV), the routing paths in modern networks are
becoming more complex. To address this problem, we propose
a novel inference problem, called the weight inference problem,
which infers the finest-granularity information from end-to-end
measurements on general routing paths in general topologies.
Our measurements are based on emulated multicast probes with
a controllable “width”. We show that the problem has a unique
solution when the multicast width is unconstrained; otherwise,
we show that the problem can be treated as a sparse approximation problem, which allows us to apply variations of the
pursuit algorithms. Simulations based on real network topologies
show that our solution significantly outperforms a state-of-theart network tomography algorithm, and increasing the width of
multicast substantially improves the inference accuracy
Multi-source Cooperative Adaptation for QoE-aware Video Multicast Rate-control
Abstract-We consider a wide-area video conferencing application where the video sources adapt their send rates according to the available bandwidth in the network paths. We advocate a QoE-aware cooperative rate control of the sources to relieve the congestion, instead of running multiple (independent) instances of a singlesource adaptation algorithm in a QoE-oblivious manner and additively superposing their results. Our paper focuses on the architecture of such a QoE-aware video multicast system. Dove-tailed to the core functionality of rate adaptation is the session-layer configuration control mechanisms to deliver video to various end-user devices
Finding the Right Tree: Topology Inference Despite Spatial Dependences
© 1963-2012 IEEE. Network tomographic techniques have almost exclusively been built on a strong assumption of mutual independence of link processes. We introduce model classes for link loss processes with non-Trivial spatial dependencies, for which the tree topology is nonetheless identifiable from leaf measurements using multicast probing. We show that these classes are large in a well-defined sense, and we provide an algorithm, SLTD, capable of returning the correct topology with certainty in the limit of infinite data
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