69,328 research outputs found
Optimal routing on complex networks
We present a novel heuristic algorithm for routing optimization on complex
networks. Previously proposed routing optimization algorithms aim at avoiding
or reducing link overload. Our algorithm balances traffic on a network by
minimizing the maximum node betweenness with as little path lengthening as
possible, thus being useful in cases when networks are jamming due to queuing
overload. By using the resulting routing table, a network can sustain
significantly higher traffic without jamming than in the case of traditional
shortest path routing.Comment: 4 pages, 5 figure
Transport optimization on complex networks
We present a comparative study of the application of a recently introduced
heuristic algorithm to the optimization of transport on three major types of
complex networks. The algorithm balances network traffic iteratively by
minimizing the maximum node betweenness with as little path lengthening as
possible. We show that by using this optimal routing, a network can sustain
significantly higher traffic without jamming than in the case of shortest path
routing. A formula is proved that allows quick computation of the average
number of hops along the path and of the average travel times once the
betweennesses of the nodes are computed. Using this formula, we show that
routing optimization preserves the small-world character exhibited by networks
under shortest path routing, and that it significantly reduces the average
travel time on congested networks with only a negligible increase in the
average travel time at low loads. Finally, we study the correlation between the
weights of the links in the case of optimal routing and the betweennesses of
the nodes connected by them.Comment: 19 pages, 7 figure
Integrating static and dynamic information for routing traffic
The efficiency of traffic routing on complex networks can be reflected by two
key measurements i.e. the system capacity and the average data packets travel
time. In this paper, we propose a mixing routing strategy by integrating local
static and dynamic information for enhancing the efficiency of traffic on
scale-free networks. The strategy is governed by a single parameter. Simulation
results show that there exists a optimal parameter value by considering both
maximizing the network capacity and reducing the packet travel time. Comparing
with the strategy by adopting exclusive local static information, the new
strategy shows its advantages in improving the efficiency of the system. The
detailed analysis of the mixing strategy is provided. This work suggests that
how to effectively utilize the larger degree nodes plays the key role in the
scale-free traffic systems.Comment: 5 pages, 5 figure
Impact of community structure on information transfer
The observation that real complex networks have internal structure has important implication for dynamic processes occurring on such topologies. Here we investigate the impact of community structure on a model of information transfer able to deal with both search and congestion simultaneously. We show that networks with fuzzy community structure are more efficient in terms of packet delivery than those with pronounced community structure. We also propose an alternative packet routing algorithm which takes advantage of the knowledge of communities to improve information transfer and show that in the context of the model an intermediate level of community structure is optimal. Finally, we show that in a hierarchical network setting, providing knowledge of communities at the level of highest modularity will improve network capacity by the largest amount
EEGRA: Energy Efficient Geographic Routing Algorithms for Wireless Sensor Network
[[abstract]]Energy efficiency is critical in wireless sensor networks (WSN) for system reliability and deployment cost. The power consumption of the communication in multi-hop WSN is primarily decided by three factors: routing distance, signal
interference, and computation cost of routing. Several routing algorithms designed for energy efficiency or interference avoidance had been proposed. However, they are either too complex to be useful in practices or specialized for certain
WSN architectures. In this paper, we propose two energy efficient geographic routing algorithms (EEGRA) for wireless sensor networks, which are based on existing geographic routing algorithms and take all three factors into account.
The first algorithm combines the interference into the routing cost function, and uses it in the routing decision. The second algorithm transforms the problem into a constrained
optimization problem, and solves it by searching the optimal discretized interference level. We integrate four geographic routing algorithms: GOAFR+, Face Routing, GPSR, and RandHT, to both EEGRA algorithms and compare them with three other routing methods in terms of power consumption and computation cost for the grid and irregular sensor topologies. The results of our experiments show both algorithms conserve sensor’s routing energy 30% ~ 50% comparing to general geographic routing algorithms. In addition, the time complexity of EEGRA algorithms is similar to the geographic greedy routing methods, which is much faster than the optimal SINR-based algorithm.[[conferencetype]]國際[[conferencedate]]20121213~20121215[[iscallforpapers]]Y[[conferencelocation]]San Marcos, Texas, US
Deep neural networks for network routing
In this work, we propose a Deep Learning (DL) based solution to the problem of routing traffic flows in computer networks. Routing decisions can be made in different ways depending on the desired objective and, based on that objective function, optimal solutions can be computed using a variety of techniques, e.g. with mixed integer linear programming. However, determining these solutions requires solving complex optimization problems and, thus, cannot be typically done at runtime. Instead, heuristics for these problems are often created but designing them is non-trivial in many cases. The routing framework proposed here presents an alternative to the design of heuristics, whilst still achieving good performance. This is done by building a DL model trained on the optimal decisions over flows from known traffic demands. To evaluate our solution, we focused on the problem of network congestion, even though a wide range of alternative objectives could be fitted into this framework. We ran experiments using two publicly available datasets of networks with real traffic demands and showed that our solution achieves close-to-optimal network congestion values.This research was sponsored by the U.S. Army Research
Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001.info:eu-repo/semantics/publishedVersio
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