Research On The Optimization Strategy Of Traffic Capacity Based On BA Scale-free Networks

Now,people's production and life are closely related to various types of infrastructure networks such as communication networks,transportation networks and power grids.The most important functions of these networks are transmissions,including data,materials,power,etc.Society has entered an era of big data and large-scale traffic.For example,with the rapid development of information technology and the increasingly abundance of online entertainment projects,the number of online users is increasing,and the amount of data on the Internet has experienced explosive growth;with the general improvement of the social and economic levels,the number of road vehicles has increased significantly and the traffic flow has remained high.These changes have caused a large amount of transmissions that cannot be completed in time,resulting in network congestion.Network traffic capacity is the maximum traffic that the network can handle without congestion,and increasing network traffic capacity is a necessary means to alleviate network congestion.With the discovery of small-world features and scale-free features,the research on complex networks has developed rapidly.Complex network theory has become the basic tool for understanding and describing the property and functions of actual networks.By analyzing the dynamic transmission process of data flow on the network,a complex network model is used to model it,and a strategy that can effectively improve the network traffic capacity is proposed,thereby reducing the occurrence of network congestion.At present,there are three main factors that affect network traffic capacity: network topology structure,routing strategy,and resource allocation.In the research of this thesis,assuming that the network resources are infinite and evenly distributed,the analysis and research are mainly focused on optimizing the network topology and routing strategy,and two effective strategies are proposed to improve the network traffic capacity.The main work of this thesis is as follows:(1)In terms of optimizing network topology structure,an effective link-removal strategy is proposed.The strategy combines two central measures of local centrality and closeness centrality,taking into account both the local and global information of the network.By appropriately deleting a proportion of links with high load in the network,the data flow bypasses the center node and selects other links with lower loads for transmission,so that the data flow in the network tends to be uniform and the overall network performance is improved.Simulation experiments verify the effectiveness of this strategy in improving network traffic capacity.In the BA scale-free network,the shortest path routing strategy is used to compare it with three existing link-removal strategies.The experimental results show that compared with other strategies,the link-removal strategy proposed in this thesis can improve the network traffic capacity more effectively,and the average shortest path length is not greater than other strategies.(2)In terms of optimizing network routing strategies,an improved routing algorithm based on maximum betweenness is proposed.Since the maximum betweenness is inversely proportional to the traffic capacity of the network,the minimum value of the maximum betweenness is the optimal value for improving the traffic capacity of the network.In this thesis,by improving the optimal routing strategy,the approximate minimum of the maximum betweenness is obtained.In this algorithm,at each iteration,increasing the weight of node whose betweenness is close to the maximum betweenness,so that the sum of the node weight is relatively large on the path through the center node,but the data flow always selects the path with the smallest sum of the weight for transmission.Therefore,the algorithm will bypass the central node,thereby reducing network congestion.The simulation experiment results show that this algorithm speeds up the convergence of the maximum betweenness,reduces the convergence time,and does not come at the cost of reducing the network traffic capacity.