SDN驱动的网络流量负载均衡路由优化算法

Traffic load balancing routing optimization algorithms in SDN-driven networks

在5G网络数据流量剧增的背景下,针对5G网络流量负载均衡问题提出并评估了两种基于软件定义网络驱动的路由搜索优化算法。首先,建立了软件定义网络多约束数据传输路径选择模型;然后,针对所提模型提出了一种流量负载均衡广度优先搜索(load balancing scheme with breadth-first-search,LBB)路径优化算法,在广度优先搜索的过程中,设定一个动态流量阈值对链路进行实时监测,旨在寻找源节点到目标节点的最优数据传输路径。为了减少甚至避免不必要的搜索所造成的空间开销,进一步提出了基于深度优先搜索的迭代深化搜索(iterative deepening search with depth first search,IDDFS)路径优化算法,该算法限制了数据传输路径的每次搜索深度,并在搜索过程中优先选择可用带宽最大的链路进行深度优先搜索迭代优化。仿真结果表明了所提算法在资源利用率和网络吞吐量这两项关键性能指标上的优越性能。

Under the context of the explosion of the traffic data in 5G network,two software-defined networking-driven(SDN)routing searching optimization algorithms are proposed and evaluated to solve the traffic load balancing problems of the traffic data in 5G network.Firstly,a multi-constraints data transmission routing selection model of SDN is established.Secondly,a traffic load balancing scheme with breadth-first-search(LBB)routing optimization algorithm is proposed to solve the model.In the process of the breadth-first-search,LBB sets a dynamic traffic threshold to monitor the link in real time,which aims to find the optimal data transmission routing from the source node to the target node.To further reduce or even avoid the space overhead caused by the unnecessary searching,an iterative deepening search with depth-first-search(IDDFS)routing optimization algorithm is proposed,which selects the link with the highest available bandwidth in the searching process.During the routing searching,IDDFS optimizes the iterative optimization with the policy of depth-first-search.Simulation results show that the proposed algorithms have the excellent performance in resource utilization and network throughput.