SDN驱动的网络流量负载均衡路由优化算法

doi:10.12305/j.issn.1001-506X.2023.08.33

摘要/Abstract

摘要：

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。

关键词: 5G网络, 软件定义网络, 流量负载均衡, 路由算法

Abstract:

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.

Key words: 5G network, software-defined networking, traffic load balancing, routing algorithm

中图分类号:

TN92

闵晓飞, 李靖, 张朝辉. SDN驱动的网络流量负载均衡路由优化算法[J]. 系统工程与电子技术, 2023, 45(8): 2578-2587.

Xiaofei MIN, Jing LI, Zhaohui ZHANG. Traffic load balancing routing optimization algorithms in SDN-driven networks[J]. Systems Engineering and Electronics, 2023, 45(8): 2578-2587.

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符号	含义
C	接入网络用户c的集合
O	网络中所有虚拟节点的集合
R	超级源节点
S	终端服务器
C_S	接入服务器S的客户端集合
\|C_S\|	C_S中元素的个数
T_{i, j}	从节点i转发到节点j的流量
F_{i, j}^S	从客户端流向服务器的数据流
Φ	激活的虚拟节点集合
λ_t	第t个用户申请接入的带宽
η(u)	图G中节点u的邻居节点

算法	OVS
算法	平均使用数	最大利用率/%	平均利用率/%
HPR	11.6	36	33.4
LBB	12.7	56	36.7
IDDFS	15.7	52	46.0
Dijkstra	13.0	40	37.4

算法	OVS
算法	平均使用数	最大利用率/%	平均利用率/%
HPR	11.7	36.0	33.8
LBB	16.6	40.0	47.9
IDDFS	25.6	80.0	73.8
Dijkstra	12.3	37.5	35.0

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