938 research outputs found
Multicast-based inference of network-internal loss performance
©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.The use of multicast traffic as measurement probes is efficient and effective to infer network-internal characteristics. We propose a new statistical approach to infer network internal link loss performance from end-to-end measurements. Incorporating with the procedure of topology inference, we present an inference algorithm that can infer loss rates of individual links in the network when it infers the network topology. It is proved that the loss rate inferred by our approach is consistent with the real loss rate as the number of probe packets tends to infinity. The approach is also extended to general trees case for loss performance inference. Loss rate-based scheme on topology inference is built in view of correct convergence to the true topology for general trees.Hui Tian, Hong She
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
A Network Coding Approach to Loss Tomography
Network tomography aims at inferring internal network characteristics based
on measurements at the edge of the network. In loss tomography, in particular,
the characteristic of interest is the loss rate of individual links and
multicast and/or unicast end-to-end probes are typically used. Independently,
recent advances in network coding have shown that there are advantages from
allowing intermediate nodes to process and combine, in addition to just
forward, packets. In this paper, we study the problem of loss tomography in
networks with network coding capabilities. We design a framework for estimating
link loss rates, which leverages network coding capabilities, and we show that
it improves several aspects of tomography including the identifiability of
links, the trade-off between estimation accuracy and bandwidth efficiency, and
the complexity of probe path selection. We discuss the cases of inferring link
loss rates in a tree topology and in a general topology. In the latter case,
the benefits of our approach are even more pronounced compared to standard
techniques, but we also face novel challenges, such as dealing with cycles and
multiple paths between sources and receivers. Overall, this work makes the
connection between active network tomography and network coding
Network Tomography: Identifiability and Fourier Domain Estimation
The statistical problem for network tomography is to infer the distribution
of , with mutually independent components, from a measurement model
, where is a given binary matrix representing the
routing topology of a network under consideration. The challenge is that the
dimension of is much larger than that of and thus the
problem is often called ill-posed. This paper studies some statistical aspects
of network tomography. We first address the identifiability issue and prove
that the distribution is identifiable up to a shift parameter
under mild conditions. We then use a mixture model of characteristic functions
to derive a fast algorithm for estimating the distribution of
based on the General method of Moments. Through extensive model simulation and
real Internet trace driven simulation, the proposed approach is shown to be
favorable comparing to previous methods using simple discretization for
inferring link delays in a heterogeneous network.Comment: 21 page
Discover multicast network internal characteristics based on Hamming distance
Copyright © 2005 IEEEOne of the important techniques to monitor and control large-scale networks today is to implement only at the end. However end-based control needs to have the knowledge of network internal characteristics. The paper proposes a novel approach to discover network internal characteristics from end-to-end multicast traffic measurements, which requires no support from internal routers. Our approach is based on Hamming distance of sequences on receipt/loss of probe packets maintained at each pair of nodes. As we discuss in this paper, our approach mainly focuses on identification of network internal characteristics of routing topology and loss performance. The simulation shows that the Hamming distance-based approach can discover the routing topology which is more accurate and efficient with a finite number of probe packets than before. The Hamming distance matrix proposed in this paper can also effectively discover the loss performance of the network.Hui Tian, Hong She
Network monitoring in multicast networks using network coding
In this paper we show how information contained in robust network codes can be used for passive inference of possible locations of link failures or losses in a network. For distributed randomized network coding, we bound the probability of being able to distinguish among a given set of failure events, and give some experimental results for one and two link failures in randomly generated networks. We also bound the required field size and complexity for designing a robust network code that distinguishes among a given set of failure events
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