摘要
软件定义网络(SDN)中,流表项是由控制器创建并指导交换机处理数据包的转发规则。流表项保存在交换机的内存并有一定的超时时间,会影响SDN控制通道的带宽消耗、交换机的内存消耗以及系统资源和性能的管理。针对现有SDN性能优化方案大多为单一目标优化,未考虑流表项超时类型和时间对不同优化目标的影响,提出一种基于流表项动态混合超时的多目标优化方案,对大象流的侦测精度、流表项的交换机内存消耗和控制通道带宽占用进行三目标联合优化。动态混合超时将现有的两种流表项超时方式,即硬超时和空闲超时相结合,并对流表项的超时类型和时间进行双维度动态调节。通过NSGA-Ⅱ算法求解所提优化问题,评估不同超时方式和超时时间对三个优化目标的影响,并通过合并特定超时时间下的解集与贝叶斯多目标优化算法的解集对NSGA-Ⅱ算法的解集质量进行改进。结果表明,所提方案能提供更高的侦测精度、更低的带宽占用和更小的交换机内存消耗,明显提升了SDN的综合性能。
Flow entries are forwarding rules generated by controllers and guide switches to process data packets in Software Defined Network(SDN).Every flow entry is stored in the memory of switches and has timeout,which affects the bandwidth cost in SDN control channel,the memory consumption in switches,and the system’s resource management and performance.As most of the existing SDN performance optimization schemes only have single objective,and do not consider the impact of the types and time of the flow entry timeouts,a multi-objective optimization scheme was proposed based on the dynamic mixed timeouts of flow entries to simultaneously optimize the three objects:the detection of elephant flows,the memory consumption of flow entries in switches,and the control channel bandwidth occupation.In the dynamic mixed timeout,hard-timeout and idle-timeout,two timeout methods of flow entries were combined,and the timeout type and time of flow entries were adjusted in a two-dimensional dynamic way.The NSGA-Ⅱalgorithm was used to solve the proposed optimization problem and to evaluate the impact of different timeout methods and timeout time on the three optimization objectives.The solution set of specific timeouts was combined with the solution set of Bayesian multi-objective optimization algorithm to improve the quality of the solution set.The results show that the proposed scheme can provide a higher detection accuracy,a lower bandwidth occupation,and a smaller switch memory consumption.It significantly improves the overall performance of SDNs.
作者
马晓航
廖灵霞
李智
秦斌
赵涵捷
MA Xiaohang;LIAO Lingxia;LI Zhi;QIN Bin;CHAO Han-chieh(School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin Guanxi 541004,China;School of Electronic Information and Automation,Guilin University of Aerospace Technology,Guilin Guanxi 541004,China;Department of Electrical Engineering,Taiwan Dong Hwa University,Hualien Taiwan 974301,China;Information Center,Guilin University of Aerospace Technology,Guilin Guanxi 541004,China)
出处
《计算机应用》
CSCD
北大核心
2021年第12期3658-3665,共8页
journal of Computer Applications
基金
国家自然科学基金资助项目(61962016)。
关键词
软件定义网络
流表项超时
多目标优化
大象流侦测
网络性能优化
Software Defined Network(SDN)
flow entry timeout
multi-objective optimization
elephant flow detection
network performance optimization
