58,615 research outputs found
Brief Announcement: Minimizing Congestion in Hybrid Demand-Aware Network Topologies
Emerging reconfigurable optical communication technologies enable demand-aware networks: networks whose static topology can be enhanced with demand-aware links optimized towards the traffic pattern the network serves. This paper studies the algorithmic problem of how to jointly optimize the topology and the routing in such demand-aware networks, to minimize congestion. We investigate this problem along two dimensions: (1) whether flows are splittable or unsplittable, and (2) whether routing on the hybrid topology is segregated or not, i.e., whether or not flows either have to use exclusively either the static network or the demand-aware connections. For splittable and segregated routing, we show that the problem is 2-approximable in general, but APX-hard even for uniform demands induced by a bipartite demand graph. For unsplittable and segregated routing, we show an upper bound of O(log m/ log log m) and a lower bound of ?(log m/ log log m) for polynomial-time approximation algorithms, where m is the number of static links. Under splittable (resp., unsplittable) and non-segregated routing, even for demands of a single source (resp., destination), the problem cannot be approximated better than ?(c_{max}/c_{min}) unless P=NP, where c_{max} (resp., c_{min}) denotes the maximum (resp., minimum) capacity. It is still NP-hard for uniform capacities, but can be solved efficiently for a single commodity and uniform capacities
Modeling Evolutionary Dynamics of Lurking in Social Networks
Lurking is a complex user-behavioral phenomenon that occurs in all
large-scale online communities and social networks. It generally refers to the
behavior characterizing users that benefit from the information produced by
others in the community without actively contributing back to the production of
social content. The amount and evolution of lurkers may strongly affect an
online social environment, therefore understanding the lurking dynamics and
identifying strategies to curb this trend are relevant problems. In this
regard, we introduce the Lurker Game, i.e., a model for analyzing the
transitions from a lurking to a non-lurking (i.e., active) user role, and vice
versa, in terms of evolutionary game theory. We evaluate the proposed Lurker
Game by arranging agents on complex networks and analyzing the system
evolution, seeking relations between the network topology and the final
equilibrium of the game. Results suggest that the Lurker Game is suitable to
model the lurking dynamics, showing how the adoption of rewarding mechanisms
combined with the modeling of hypothetical heterogeneity of users' interests
may lead users in an online community towards a cooperative behavior.Comment: 13 pages, 5 figures. Accepted at CompleNet 201
Congestion-gradient driven transport on complex networks
We present a study of transport on complex networks with routing based on
local information. Particles hop from one node of the network to another
according to a set of routing rules with different degrees of congestion
awareness, ranging from random diffusion to rigid congestion-gradient driven
flow. Each node can be either source or destination for particles and all nodes
have the same routing capacity, which are features of ad-hoc wireless networks.
It is shown that the transport capacity increases when a small amount of
congestion awareness is present in the routing rules, and that it then
decreases as the routing rules become too rigid when the flow becomes strictly
congestion-gradient driven. Therefore, an optimum value of the congestion
awareness exists in the routing rules. It is also shown that, in the limit of a
large number of nodes, networks using routing based on local information jam at
any nonzero load. Finally, we study the correlation between congestion at node
level and a betweenness centrality measure.Comment: 11 pages, 8 figure
Disaster-Resilient Control Plane Design and Mapping in Software-Defined Networks
Communication networks, such as core optical networks, heavily depend on
their physical infrastructure, and hence they are vulnerable to man-made
disasters, such as Electromagnetic Pulse (EMP) or Weapons of Mass Destruction
(WMD) attacks, as well as to natural disasters. Large-scale disasters may cause
huge data loss and connectivity disruption in these networks. As our dependence
on network services increases, the need for novel survivability methods to
mitigate the effects of disasters on communication networks becomes a major
concern. Software-Defined Networking (SDN), by centralizing control logic and
separating it from physical equipment, facilitates network programmability and
opens up new ways to design disaster-resilient networks. On the other hand, to
fully exploit the potential of SDN, along with data-plane survivability, we
also need to design the control plane to be resilient enough to survive network
failures caused by disasters. Several distributed SDN controller architectures
have been proposed to mitigate the risks of overload and failure, but they are
optimized for limited faults without addressing the extent of large-scale
disaster failures. For disaster resiliency of the control plane, we propose to
design it as a virtual network, which can be solved using Virtual Network
Mapping techniques. We select appropriate mapping of the controllers over the
physical network such that the connectivity among the controllers
(controller-to-controller) and between the switches to the controllers
(switch-to-controllers) is not compromised by physical infrastructure failures
caused by disasters. We formally model this disaster-aware control-plane design
and mapping problem, and demonstrate a significant reduction in the disruption
of controller-to-controller and switch-to-controller communication channels
using our approach.Comment: 6 page
A Content-based Centrality Metric for Collaborative Caching in Information-Centric Fogs
Information-Centric Fog Computing enables a multitude of nodes near the
end-users to provide storage, communication, and computing, rather than in the
cloud. In a fog network, nodes connect with each other directly to get content
locally whenever possible. As the topology of the network directly influences
the nodes' connectivity, there has been some work to compute the graph
centrality of each node within that network topology. The centrality is then
used to distinguish nodes in the fog network, or to prioritize some nodes over
others to participate in the caching fog. We argue that, for an
Information-Centric Fog Computing approach, graph centrality is not an
appropriate metric. Indeed, a node with low connectivity that caches a lot of
content may provide a very valuable role in the network.
To capture this, we introduce acontent-based centrality (CBC) metric which
takes into account how well a node is connected to the content the network is
delivering, rather than to the other nodes in the network. To illustrate the
validity of considering content-based centrality, we use this new metric for a
collaborative caching algorithm. We compare the performance of the proposed
collaborative caching with typical centrality based, non-centrality based, and
non-collaborative caching mechanisms. Our simulation implements CBC on three
instances of large scale realistic network topology comprising 2,896 nodes with
three content replication levels. Results shows that CBC outperforms benchmark
caching schemes and yields a roughly 3x improvement for the average cache hit
rate
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