38 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
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
Inferring Link Loss Rates from Unicast-Based End-to-End Measurement
In the Internet, because of huge scale and distributed administration, it is of practical importance to infer network-internal characteristics that cannot be measured directly. In this paper, based on a general framework we proposed previously, we present a feasible method of inferring packet loss rates of individual links from end-to-end measurement of unicast probe packets. Compared with methods using multicast probes, unicast-based inference methods are more flexible and widely applicable, whereas they have a problem with imperfect correlation in concurrent events on paths. Our method can infer link loss rates under this problem, and is applicable to various path-topologies including trees, inverse trees and their combinations. We also show simulation results which indicate potential of our unicast-based method
Measuring the dynamical state of the Internet: Large-scale network tomography via the ETOMIC infrastructure
In this paper we show how to go beyond the study of the
topological properties of the Internet, by measuring its
dynamical state using special active probing techniques and the
methods of network tomography. We demonstrate this approach by
measuring the key state parameters of Internet paths, the
characteristics of queuing delay, in a part of the European
Internet. In the paper we describe in detail the ETOMIC
measurement platform that was used to conduct the experiments,
and the applied method of queuing delay tomography. The main
results of the paper are maps showing various spatial structure
in the characteristics of queuing delay corresponding to the
resolved part of the European Internet. These maps reveal that
the average queuing delay of network segments spans more than
two orders of magnitude, and that the distribution of this
quantity is very well fitted by the log-normal distribution.
Copyright © 2006 S. Karger AG
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