An efficient routing strategy on spatial scale-free networks

2014 ◽  
Vol 25 (07) ◽  
pp. 1450017 ◽  
Author(s):  
Xiang-Min Guan ◽  
Xue-Jun Zhang ◽  
Yanbo Zhu ◽  
Inseok Hwang ◽  
Deng-Feng Sun
Keyword(s):  
Spatial Scale ◽  
Euclidean Distance ◽  
Network Capacity ◽  
Traffic Load ◽  
System Throughput ◽  
Traffic Dynamics ◽  
Space Factor ◽  
Scale Free ◽  
Routing Strategy ◽  
Traffic Systems

Traffic dynamics has drawn much more attention recently, but most current research barely considers the space factor, which is of critical importance in many real traffic systems. In this paper, we focus our research on traffic dynamics of a spatial scale-free network with the restriction of bandwidth proportional to link Euclidean distance, and a new routing strategy is proposed with consideration of both Euclidean distance and betweenness centralities (BC) of edges. It is found that compared with the shortest distance path (SDP) strategy and the minimum betweenness centralities (MBC) of links strategy, our strategy under some parameters can effectively balance the traffic load and avoid excessive traveling distance which can improve the spatial network capacity and some system behaviors reflecting transportation efficiency, such as average packets traveling time, average packets waiting time and system throughput, traffic load and so on. Besides, though the restriction of bandwidth can trigger congestion, the proposed routing strategy always has the best performance no matter what bandwidth becomes. These results can provide insights for research on real networked traffic systems.

ANALYSIS OF TRAFFIC FLOW ON COMPLEX NETWORKS

2011 ◽  
Vol 25 (10) ◽  
pp. 1419-1428 ◽  
Author(s):  
KUN LI ◽  
XIAOFENG GONG ◽  
SHUGUANG GUAN ◽  
C.-H. LAI
Keyword(s):  
Complex Networks ◽  
Network Capacity ◽  
Traffic Load ◽  
Packet Routing ◽  
Traffic Dynamics ◽  
Scale Free ◽  
Routing Strategy ◽  
Scale Free Networks ◽  
New Strategy ◽  
Mixed Networks

We propose a new routing strategy for controlling packet routing on complex networks. The delivery capability of each node is adopted as a piece of local information to be integrated with the load traffic dynamics to weight the next route. The efficiency of transport on complex network is measured by the network capacity, which is enhanced by distributing the traffic load over the whole network while nodes with high handling ability bear relative heavier traffic burden. By avoiding the packets through hubs and selecting next routes optimally, most travel times become shorter. The simulation results show that the new strategy is not only effective for scale-free networks but also for mixed networks in realistic networks.

An incremental optimal routing strategy for scale-free networks

2014 ◽  
Vol 25 (09) ◽  
pp. 1450044 ◽  
Author(s):  
Zhong-Yuan Jiang
Keyword(s):  
Traffic Load ◽  
Traffic Model ◽  
Optimal Routing ◽  
Traffic Dynamics ◽  
Shortest Path Routing ◽  
Scale Free ◽  
Routing Strategy ◽  
Traffic Capacity ◽  
Real Complex ◽  
Link Congestion

The link congestion based traffic model can more accurately reveal the traffic dynamics of many real complex networks such as the Internet, and heuristically optimizing each link's weight for the shortest path routing strategy can strongly improve the traffic capacity of network. In this work, we propose an optimal routing strategy in which the weight of each link is regulated incrementally to enhance the network traffic capacity by minimizing the maximum link betweenness of any link in the network. We also estimate more suitable value of the tunable parameter β for the efficient routing strategy under the link congestion based traffic model. The traffic load of network can be significantly balanced at the expense of increasing a bit average path length or average traffic load.

Traffic dynamics on a double layer coupled network considering physical queuing

2018 ◽  
Vol 29 (12) ◽  
pp. 1850126
Author(s):  
Xiao-Yu Luo ◽  
Jie Chen ◽  
Ming Li ◽  
Mao-Bin Hu
Keyword(s):  
Double Layer ◽  
Traffic Congestion ◽  
Network Capacity ◽  
Traffic Load ◽  
Long Distance ◽  
Traffic Dynamics ◽  
Traffic Systems ◽  
Network Speed ◽  
Distance Travel ◽  
Long Distance Travel

The development of modern economy makes the problem of traffic congestion increasingly serious. Many real traffic systems can be abstracted as that a variety of networks coupled and interconnected with each other. In this paper, the traffic dynamics on a double layer coupled network system is studied based on cellular automata model considering physical queuing. We explore the effect of maximal velocities in the two layer networks on the network capacity, and the mean and standard deviation of travel time. The results show that the increase of upper network velocity is beneficial to the traffic capacity and the efficiency of long-distance travel, but will also lead to larger deviation and lower reliability. We explain the phenomena by studying the usage of upper network. Finally, we investigate the vehicle distribution by adopting the Gini coefficient. It is found that the increase of upper network speed will make the traffic load distributed more uniformly in the system.

The effect of data exchange policy on traffic flow between interconnected networks

2020 ◽  
Vol 31 (03) ◽  
pp. 2050035
Author(s):  
S. Lazfi ◽  
N. Ben Haddou ◽  
A. Rachadi ◽  
H. Ez-Zahraouy
Keyword(s):  
Traffic Flow ◽  
Data Exchange ◽  
Network Capacity ◽  
Important Place ◽  
Scale Free ◽  
Routing Strategy ◽  
Optimal Functioning ◽  
Optimal Network ◽  
Exchange Policy ◽  
Traffic Systems

In order to understand and achieve an optimal functioning in real traffic systems, the problem of congestion in complex networks takes an important place in many recent researches. In this paper, we study the effect of different types of interconnections between two scale free networks on the traffic flow. Two interconnection strategies are used: in the first, we create links between nodes chosen at random from the two subnets [Formula: see text] and [Formula: see text] and, while in the second one, we link nodes selected among the hubs of the subnets. The resulting network [Formula: see text] is under a new routing strategy inspired from the minimal traffic model introduced in [D. De Martino, Phys. Rev. E 79, 015101 (2009); S. Lamzabi, S. Lazfi, H. Ez-Zahraouy, A. Benyoussef, A. Rachadi and S. Ziti, Int. J. Mod. Phys. C 25, 1450019 (2014)]. We find that in case of this routing method, the interconnection pattern has no effect on the results. Further, to control the exchange of packets between the subnets, we propose two adjusting parameters [Formula: see text] and [Formula: see text]. The study of the variation of these parameters shows that the optimal network capacity is obtained when the two subnets are allowed to exchange data more openly.

Delivering capacity allocation strategy for traffic dynamics on scale-free networks

2019 ◽  
Vol 31 (02) ◽  
pp. 2050029
Author(s):  
Jinlong Ma ◽  
Wei Sui ◽  
Changfeng Du ◽  
Xiangyang Xu ◽  
Guanghua Zhang
Keyword(s):  
Network Capacity ◽  
Capacity Allocation ◽  
Allocation Strategy ◽  
Traffic Dynamics ◽  
Shortest Path Routing ◽  
Scale Free ◽  
Optimal Value ◽  
Scale Free Networks ◽  
Resource Allocation Strategy ◽  
Traffic Systems

The traffic dynamics of complex networks are largely determined by the node’s resource distribution. In this paper, based on the shortest path routing strategy, a node delivering capacity distribution mechanism is proposed into the traffic dynamics in Barabási and Albert (BA) scale-free networks; the efficiency of the mechanism on the network capacity measured by the critical point ([Formula: see text]) of phase transition from free flow to congestion is primarily explored. Based on the proposed strategy, the total delivering capacity is reallocated according to both degree and betweenness of each node, and an optimal value of parameter [Formula: see text] is found, leading to the maximum traffic capacity. The results of numerical experiments on scale-free networks suggest that the resource allocation strategy proposed here is capable of effectively enhancing the transmission capacity of networks. Furthermore, this study may provide novel insights into research on networked traffic systems.

Traffic Dynamics Based on Local Routing Strategy in a Weighted Scale-Free Network

2009 ◽  
pp. 733-738
Author(s):  
Mao-Bin Hu ◽  
Yong-Hong Wu ◽  
Rui Jiang ◽  
Qing-Song Wu ◽  
Wen-Xu Wang
Keyword(s):  
Scale Free Network ◽  
Traffic Dynamics ◽  
Scale Free ◽  
Routing Strategy ◽  
Free Network ◽  
Local Routing

scholarly journals Distance-Based Routing Strategy for Traffic Transport in Spatial Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Wei Huang ◽  
Xiang Pan ◽  
Xi Yang ◽  
Jianhua Zhang
Keyword(s):  
Spatial Scale ◽  
Large Scale ◽  
Spatial Information ◽  
Good Choice ◽  
Spatial Distance ◽  
Greedy Routing ◽  
Scale Free ◽  
Routing Strategy ◽  
Scale Free Networks ◽  
Large Scale Networks

It is well known that routing strategies based on global topological information is not a good choice for the enhancement of traffic throughput in large-scale networks due to the heavy communication cost. On the contrary, acquiring spatial information, such as spatial distances among nodes, is more feasible. In this paper, we propose a novel distance-based routing strategy in spatial scale-free networks, called LDistance strategy. The probability of establishing links among nodes obeys the power-law in the spatial network under study. Compared with the LDegree strategy (Wang et al., 2006) and the mixed strategy (a strategy combining both greedy routing strategy and random routing strategy), results show that our proposed LDistance strategy can further enhance traffic capacity. Besides, the LDistance strategy can also achieve a much shorter delivering time than the LDegree strategy. Analyses reveal that the superiority of our strategy is mainly due to the interdependent relationship between topological and spatial characteristics in spatial scale-free networks. Furthermore, along transporting path in the LDistance strategy, the spatial distance to destination decays more rapidly, and the degrees of routers are higher than those in the LDegree strategy.

Limited static and dynamic delivering capacity allocations in scale-free networks

2017 ◽  
Vol 28 (11) ◽  
pp. 1750140 ◽  
Author(s):  
N. Ben Haddou ◽  
H. Ez-Zahraouy ◽  
A. Rachadi
Keyword(s):  
Dynamic Properties ◽  
Minimum Cost ◽  
Network Capacity ◽  
Capacity Allocation ◽  
Traffic Information ◽  
Node Degree ◽  
Scale Free ◽  
Routing Strategy ◽  
The Impact ◽  
Allocation Strategies

In traffic networks, it is quite important to assign proper packet delivering capacities to the routers with minimum cost. In this respect, many allocation models based on static and dynamic properties have been proposed. In this paper, we are interested in the impact of limiting the packet delivering capacities already allocated to the routers; each node is assigned a packet delivering capacity limited by the maximal capacity [Formula: see text] of the routers. To study the limitation effect, we use two basic delivering capacity allocation models; static delivering capacity allocation (SDCA) and dynamic delivering capacity allocation (DDCA). In the SDCA, the capacity allocated is proportional to the node degree, and for DDCA, it is proportional to its queue length. We have studied and compared the limitation of both allocation models under the shortest path (SP) routing strategy as well as the efficient path (EP) routing protocol. In the SP case, we noted a similarity in the results; the network capacity increases with increasing [Formula: see text]. For the EP scheme, the network capacity stops increasing for relatively small packet delivering capability limit [Formula: see text] for both allocation strategies. However, it reaches high values under the limited DDCA before the saturation. We also find that in the DDCA case, the network capacity remains constant when the traffic information available to each router was updated after long period times [Formula: see text].

IMPROVED EFFICIENT ROUTING STRATEGY ON SCALE-FREE NETWORKS

2012 ◽  
Vol 23 (02) ◽  
pp. 1250016 ◽  
Author(s):  
ZHONG-YUAN JIANG ◽  
MAN-GUI LIANG
Keyword(s):  
Network Traffic ◽  
Traffic Congestion ◽  
Heavy Traffic ◽  
Traffic Load ◽  
Transport Efficiency ◽  
Scale Free ◽  
Network Transport ◽  
Routing Strategy ◽  
Traffic Capacity ◽  
Path Cost

Since the betweenness of nodes in complex networks can theoretically represent the traffic load of nodes under the currently used routing strategy, we propose an improved efficient (IE) routing strategy to enhance to the network traffic capacity based on the betweenness centrality. Any node with the highest betweenness is susceptible to traffic congestion. An efficient way to improve the network traffic capacity is to redistribute the heavy traffic load from these central nodes to non-central nodes, so in this paper, we firstly give a path cost function by considering the sum of node betweenness with a tunable parameter β along the actual path. Then, by minimizing the path cost, our IE routing strategy achieved obvious improvement on the network transport efficiency. Simulations on scale-free Barabási–Albert (BA) networks confirmed the effectiveness of our strategy, when compared with the efficient routing (ER) and the shortest path (SP) routing.

AN EFFICIENT WEIGHTED ROUTING STRATEGY FOR SCALE-FREE NETWORKS

2012 ◽  
Vol 26 (29) ◽  
pp. 1250195 ◽  
Author(s):  
ZHONG-YUAN JIANG ◽  
MAN-GUI LIANG ◽  
JIAN-LING HUANG ◽  
QIAN LI
Keyword(s):  
Traffic Congestion ◽  
Heavy Traffic ◽  
Traffic Load ◽  
Shortest Path Routing ◽  
Scale Free ◽  
Routing Strategy ◽  
Traffic Capacity ◽  
Edge Betweenness ◽  
Real Complex ◽  
Link Congestion

Considering the link congestion based traffic model, which can more accurately model the traffic diffusing process of many real complex systems such as the Internet, we propose an efficient weighted routing strategy in which each link's weight is assigned with the edge betweenness of the original un-weighted network with a tunable parameter α. As the links with the highest edge betweenness are susceptible to traffic congestion, our routing strategy efficiently redistribute the heavy traffic load from central links to noncentral links. The highest traffic capacity under this new routing strategy is achieved when compared with the shortest path routing strategy and the efficient routing strategy. Moreover, the average path length of our routing strategy is much smaller than that of the efficient routing strategy. Therefore, our weighted routing strategy is preferable to other routing strategies and can be easily implemented through software method.

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