Global Dynamic One-Step-Prediction Resource Allocation Strategy for Space Stereo Multi-layer Data Asymmetric Scale-Free Network

An efficient routing strategy can deliver packets quickly to improve the network capacity. Node congestion and transmission path length are inevitable real-time factors for a good routing strategy. Existing dynamic global routing strategies only consider the congestion of neighbor nodes and the shortest path, which ignores other key nodes’ congestion on the path. With the development of detection methods and techniques, global traffic information is readily available and important for the routing choice. Reasonable use of this information can effectively improve the network routing. So, an improved global dynamic routing strategy is proposed, which considers the congestion of all nodes on the shortest path and incorporates the waiting time of the most congested node into the path. We investigate the effectiveness of the proposed routing for scale-free network with different clustering coefficients. The shortest path routing strategy and the traffic awareness routing strategy only considering the waiting time of neighbor node are analyzed comparatively. Simulation results show that network capacity is greatly enhanced compared with the shortest path; congestion state increase is relatively slow compared with the traffic awareness routing strategy. Clustering coefficient increase will not only reduce the network throughput, but also result in transmission average path length increase for scale-free network with tunable clustering. The proposed routing is favorable to ease network congestion and network routing strategy design.

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Bandwidth allocation strategy for traffic systems of scale-free network

Physics Letters A ◽

10.1016/j.physleta.2010.10.029 ◽

2010 ◽

Vol 374 (48) ◽

pp. 4825-4830 ◽

Cited By ~ 39

Author(s):

Xiang Ling ◽

Mao-Bin Hu ◽

Wen-Bo Du ◽

Rui Jiang ◽

Yong-Hong Wu ◽

...

Keyword(s):

Bandwidth Allocation ◽

Scale Free Network ◽

Allocation Strategy ◽

Scale Free ◽

Free Network ◽

Traffic Systems

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QUEUE RESOURCE REALLOCATION STRATEGY FOR TRAFFIC SYSTEMS IN SCALE-FREE NETWORK

International Journal of Modern Physics C ◽

10.1142/s0129183113500137 ◽

2013 ◽

Vol 24 (03) ◽

pp. 1350013 ◽

Cited By ~ 19

Author(s):

SHUAI ZHANG ◽

MAN-GUI LIANG ◽

ZHONG-YUAN JIANG ◽

HUI-JIA LI

Keyword(s):

Queue Length ◽

Communication Systems ◽

Allocation Strategy ◽

Resource Reallocation ◽

Physical Constraints ◽

Scale Free ◽

Traffic Capacity ◽

Free Network ◽

Resource Allocation Strategy ◽

Traffic Systems

In real communication systems, each node has a finite queue length to store packets due to physical constraints. In this paper, we propose a queue resource allocation strategy for traffic dynamics in scale-free networks. With a finite resource of queue, the allocation of queue length on node i is based on Bi, where Biis the generalized betweenness centrality of node i. The overall traffic capacity of a network system can be evaluated by the critical packet generating rate (Rc). Through the use of the proposed queue allocation scheme for the shortest path protocol and efficient routing protocol, our strategy performs better than the uniform queue length allocation strategy, which is demonstrated by a larger value of the critical generating rate. We also give a method to estimate the network traffic capacity theoretically.

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Node influence identification via resource allocation dynamics

International Journal of Modern Physics C ◽

10.1142/s012918311450065x ◽

2014 ◽

Vol 25 (11) ◽

pp. 1450065 ◽

Cited By ~ 2

Author(s):

Shu-Jiao Ma ◽

Zhuo-Ming Ren ◽

Chun-Ming Ye ◽

Qiang Guo ◽

Jian-Guo Liu

Keyword(s):

Resource Allocation ◽

Complex Networks ◽

Network Structure ◽

Decomposition Method ◽

Scale Free Network ◽

Improved Method ◽

Exponential Form ◽

Ranking List ◽

Scale Free ◽

Free Network

Identifying the node influence in complex networks is an important task to optimally use the network structure and ensure the more efficient spreading in information. In this paper, by taking into account the resource allocation dynamics (RAD) and the k-shell decomposition method, we present an improved method namely RAD to generate the ranking list to evaluate the node influence. First, comparing with the epidemic process results for four real networks, the RAD method could identify the node influence more accurate than the ones generated by the topology-based measures including the degree, k-shell, closeness and the betweenness. Then, a growing scale-free network model with tunable assortative coefficient is introduced to analyze the effect of the assortative coefficient on the accuracy of the RAD method. Finally, the positive correlation is found between the RAD method and the k-shell values which display an exponential form. This work would be helpful for deeply understanding the node influence of a network.

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