GPU OpenFlow海量数据网络处理模型——GOMDI  

GOMDI: GPU OpenFlow massive data network analysis model

作　　者：张伟[1] 解争龙[1] 丁要军[1,2] 张潇晓[2] 

机构地区：[1]咸阳师范学院信息工程学院,陕西咸阳712000 [2]西北工业大学计算机学院,西安710072

出　　处：《计算机应用》2014年第8期2243-2247,2272,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61102018);陕西省教育厅科研计划项目(12JK0933);陕西省科技厅科研计划项目(2013JM8037);咸阳师范学院专项科研基金资助项目(12XSYK068)

摘　　要：OpenFlow的出现提高了现有网络的服务质量(QoS),但在处理海量数据时存在网络会话识别效率低、网络报文转发路径不佳等缺点。在OpenFlow的研究基础上,提出了海量网络数据处理(GOMDI)模型,通过将GPU并行计算、生物序列算法和机器学习方法相融合,设计出GOMDI网络会话匹配算法和路径选择算法。实验结果表明,GOMDI网络会话匹配算法与CPU环境相比加速比提升了近300;路径选择算法中网络丢包率低于5%,网络延时小于20 ms。因此,GOMDI模型可有效地提升网络性能,满足大数据环境下实时处理海量信息的需求。OpenFlow enhances the Quality of Service （QoS） of traditional networks, but it has disadvantage that its network session identification efficiency is low and the network packet forwarding path is poor and so on. On the basis of the current study of the OpenFlow, GPU OpenFlow Massive Data Network Analysis （GOMDI） model was proposed by this paper, through integrating the biological sequence algorithm, GPU parallel computing algorithm and machine learning methods. The network session matching algorithm and path selection algorithm of GOMDI were designed. The experimental results show that the speedup of the GOMDI network session matching algorithm is over 300 higher than the CPU environment in real network, and the network packet loss rate of its path selection algorithm is lower than 5%, the network delay is less than 20ms. Thus, the GOMDI model can effectively improve network performance and meet the needs of the real-time processing for massive information in big data environment.

关 键 词：OpenFlow GPU 生物序列 机器学习 

分 类 号：TP393.02[自动化与计算机技术—计算机应用技术]