5,197 research outputs found
Self-Healing Protocols for Connectivity Maintenance in Unstructured Overlays
In this paper, we discuss on the use of self-organizing protocols to improve
the reliability of dynamic Peer-to-Peer (P2P) overlay networks. Two similar
approaches are studied, which are based on local knowledge of the nodes' 2nd
neighborhood. The first scheme is a simple protocol requiring interactions
among nodes and their direct neighbors. The second scheme adds a check on the
Edge Clustering Coefficient (ECC), a local measure that allows determining
edges connecting different clusters in the network. The performed simulation
assessment evaluates these protocols over uniform networks, clustered networks
and scale-free networks. Different failure modes are considered. Results
demonstrate the effectiveness of the proposal.Comment: The paper has been accepted to the journal Peer-to-Peer Networking
and Applications. The final publication is available at Springer via
http://dx.doi.org/10.1007/s12083-015-0384-
On the Topology Maintenance of Dynamic P2P Overlays through Self-Healing Local Interactions
This paper deals with the use of self-organizing protocols to improve the
reliability of dynamic Peer-to-Peer (P2P) overlay networks. We present two
approaches, that employ local knowledge of the 2nd neighborhood of nodes. The
first scheme is a simple protocol requiring interactions among nodes and their
direct neighbors. The second scheme extends this approach by resorting to the
Edge Clustering Coefficient (ECC), a local measure that allows to identify
those edges that connect different clusters in an overlay. A simulation
assessment is presented, which evaluates these protocols over uniform networks,
clustered networks and scale-free networks. Different failure modes are
considered. Results demonstrate the viability of the proposal.Comment: A revised version of the paper appears in Proc. of the IFIP
Networking 2014 Conference, IEEE, Trondheim, (Norway), June 201
Graph Annotations in Modeling Complex Network Topologies
The coarsest approximation of the structure of a complex network, such as the
Internet, is a simple undirected unweighted graph. This approximation, however,
loses too much detail. In reality, objects represented by vertices and edges in
such a graph possess some non-trivial internal structure that varies across and
differentiates among distinct types of links or nodes. In this work, we
abstract such additional information as network annotations. We introduce a
network topology modeling framework that treats annotations as an extended
correlation profile of a network. Assuming we have this profile measured for a
given network, we present an algorithm to rescale it in order to construct
networks of varying size that still reproduce the original measured annotation
profile.
Using this methodology, we accurately capture the network properties
essential for realistic simulations of network applications and protocols, or
any other simulations involving complex network topologies, including modeling
and simulation of network evolution. We apply our approach to the Autonomous
System (AS) topology of the Internet annotated with business relationships
between ASs. This topology captures the large-scale structure of the Internet.
In depth understanding of this structure and tools to model it are cornerstones
of research on future Internet architectures and designs. We find that our
techniques are able to accurately capture the structure of annotation
correlations within this topology, thus reproducing a number of its important
properties in synthetically-generated random graphs
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