| 引用本文: | 刘向举,徐杨洋,方贤进,赵 犇.软件定义网络下两阶段大象流识别算法(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2024，41（3）：89-97 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 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。 |
| 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 |
| DOI： |
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| 基金项目: |
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| Two-stage Elephant Flow Recognition Algorithm in Software Defined Network |
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LIU Xiangju,XU Yangyang,FANG Xianjin,ZHAO Ben
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School of Computer Science and Engineering Anhui University of Science and Technology Anhui Huainan 232001
China
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| Abstract: |
| 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. |
| Key words: data center network software-defined network elephant flows Gaussian distribution optimal threshold |
