32,977 research outputs found
Discovering network topology by correlating end-to-end delay measurements
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (leaves 88-89).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Conventional methods of discovering network topology require the cooperation of network elements. We present a method of network topology discovery based solely upon end-to-end delay measurements that requires only the cooperation of end systems. Previous work using end-to-end measurements has focused on discovering tree topologies; the method here discovers more general networks. The discovery method is based on two algorithms: the matroid algorithm and the correlation algorithm. This work develops and validates the correlation algorithm for three increasingly sophisticated network models using simulation. We also develop an indicator of the quality of a particular result.by Jason Ivan Howard Baron.M.Eng
Topology Discovery of Sparse Random Graphs With Few Participants
We consider the task of topology discovery of sparse random graphs using
end-to-end random measurements (e.g., delay) between a subset of nodes,
referred to as the participants. The rest of the nodes are hidden, and do not
provide any information for topology discovery. We consider topology discovery
under two routing models: (a) the participants exchange messages along the
shortest paths and obtain end-to-end measurements, and (b) additionally, the
participants exchange messages along the second shortest path. For scenario
(a), our proposed algorithm results in a sub-linear edit-distance guarantee
using a sub-linear number of uniformly selected participants. For scenario (b),
we obtain a much stronger result, and show that we can achieve consistent
reconstruction when a sub-linear number of uniformly selected nodes
participate. This implies that accurate discovery of sparse random graphs is
tractable using an extremely small number of participants. We finally obtain a
lower bound on the number of participants required by any algorithm to
reconstruct the original random graph up to a given edit distance. We also
demonstrate that while consistent discovery is tractable for sparse random
graphs using a small number of participants, in general, there are graphs which
cannot be discovered by any algorithm even with a significant number of
participants, and with the availability of end-to-end information along all the
paths between the participants.Comment: A shorter version appears in ACM SIGMETRICS 2011. This version is
scheduled to appear in J. on Random Structures and Algorithm
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
Endpoint-transparent Multipath Transport with Software-defined Networks
Multipath forwarding consists of using multiple paths simultaneously to
transport data over the network. While most such techniques require endpoint
modifications, we investigate how multipath forwarding can be done inside the
network, transparently to endpoint hosts. With such a network-centric approach,
packet reordering becomes a critical issue as it may cause critical performance
degradation.
We present a Software Defined Network architecture which automatically sets
up multipath forwarding, including solutions for reordering and performance
improvement, both at the sending side through multipath scheduling algorithms,
and the receiver side, by resequencing out-of-order packets in a dedicated
in-network buffer.
We implemented a prototype with commonly available technology and evaluated
it in both emulated and real networks. Our results show consistent throughput
improvements, thanks to the use of aggregated path capacity. We give
comparisons to Multipath TCP, where we show our approach can achieve a similar
performance while offering the advantage of endpoint transparency
A First Step Towards Automatically Building Network Representations
To fully harness Grids, users or middlewares must have some knowledge on the
topology of the platform interconnection network. As such knowledge is usually
not available, one must uses tools which automatically build a topological
network model through some measurements. In this article, we define a
methodology to assess the quality of these network model building tools, and we
apply this methodology to representatives of the main classes of model builders
and to two new algorithms. We show that none of the main existing techniques
build models that enable to accurately predict the running time of simple
application kernels for actual platforms. However some of the new algorithms we
propose give excellent results in a wide range of situations
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