摘要
目的针对数据中心网络(Data Center Network,DCN)中数据流量多导致大象流与老鼠流识别精确度低的问题,提出一种基于软件定义网络(Software Defined Networking,SDN)下两阶段大象流识别算法。方法将SDN与DCN结合,第一阶段,采用高斯分布动态阈值优化算法,通过对数据包阈值的设定,计算大象流误检率与漏检率,不断优化得到最优阈值,以此识别出可疑大象流;第二阶段,在依据流传输速率与流持续时间精确得到大象流的基础上,提出阈值约束、流量检测机制、Count计数器等三方面改进对大象流识别阈值下限的约束,将网络中大象流的数据量与流持续时间进行周期内阈值计算,提高大象流的识别精确度。结果实验结果表明:算法与已有相关算法相比,第一阶段可疑大象流平均字节数比网络流平均字节数多11.3%;不同阈值下的算法准确度提高1.7%,不同网络流量下的大象流平均检测时间降低至6 ms以内。结论软件定义网络下两阶段大象流识别算法在第一阶段具有较强的大象流识别能力,同时算法的精确度有所提高,大象流的平均检测时间降低,提高了网络质量,能为进行网络流量调度策略的进一步研究提供相关性条件。
Objective Aiming at the problem that the high data traffic in Data Center Network(DCN)leads to the low recognition accuracy of elephant flow and rat flow,a two-stage elephant flow recognition algorithm based on Software Defined Networking(SDN)was proposed.Methods Combining SDN and DCN,the dynamic threshold optimization algorithm of Gaussian distribution was adopted in the first stage to calculate the false detection rate and missing rate of elephant flows by setting the packet threshold,and the optimal threshold was continuously optimized to identify the suspicious elephant flows.In the second stage,based on the accurate elephant flow obtained from the suspicious elephant flow in the previous stage according to the transmission rate and duration of the flow,the threshold constraint,traffic detection mechanism and Count counter were proposed to improve the constraint of the lower limit of the elephant flow identification threshold,and the data volume and duration of the elephant flow in the network were calculated as the threshold value within the cycle,so as to improve the identification accuracy of elephant flows.Results The experimental results showed that compared with the existing algorithms,the average number of bytes of the suspicious elephant flows in the first stage was 11.3%more than that of the network flows.Under different thresholds,the accuracy of the algorithm was increased by 1.7%,and the average detection time under different network traffic was reduced to less than 6 ms.Conclusion The two-stage elephant flows recognition algorithm in the software-defined network has strong elephant flow recognition ability in the first stage.At the same time,the accuracy of the algorithm is improved,the average detection time of the elephant flows is reduced,and the network quality is improved,providing relevant conditions for further study of the network traffic scheduling strategy.
作者
刘向举
徐杨洋
方贤进
赵犇
LIU Xiangju;XU Yangyang;FANG Xianjin;ZHAO Ben(School of Computer Science and Engineering,Anhui University of Science and Technology,Anhui Huainan 232001,China)
出处
《重庆工商大学学报（自然科学版）》
2024年第3期89-97,共9页
Journal of Chongqing Technology and Business University:Natural Science Edition
基金
国家自然科学基金项目(61572034)
安徽省科技重大专项(18030901025).
关键词
数据中心网络
软件定义网络
大象流
高斯分布
最优阈值
data center network
software-defined network
elephant flows
Gaussian distribution
optimal threshold
