CAJ | 학술논문

随着计算机网络的不断发展以及人们对网络性能要求的不断提高,现有网络很难满足人们的需要.OpenFlow的出现能够很好地解决现有网络的不足,但存在网络会话识别效率不高,网络报文转发路径选择不佳等问题.针对匹配算法和路径转发进行了研究,提出了GODP(GPU OpenFlow data processing)模型.该模型通过融合GPU计算与生物序列算法和机器学习方法,提出了GPTWF网络会话匹配算法和网络会话转发算法,有效提升了匹配效率,优化了网络环境.实验表明网络会话匹配算法加速比提升近290,网络会话转发算法使得链路丢包率低于5%,延时小于20 ms,网络会话丢包率和延时分别平均下降62.71%和73.88%.
수착계산궤망락적불단발전이급인문대망락성능요구적불단제고,현유망락흔난만족인문적수요.OpenFlow적출현능구흔호지해결현유망락적불족,단존재망락회화식별효솔불고,망락보문전발로경선택불가등문제.침대필배산법화로경전발진행료연구,제출료GODP(GPU OpenFlow data processing)모형.해모형통과융합GPU계산여생물서렬산법화궤기학습방법,제출료GPTWF망락회화필배산법화망락회화전발산법,유효제승료필배효솔,우화료망락배경.실험표명망락회화필배산법가속비제승근290,망락회화전발산법사득련로주포솔저우5%,연시소우20 ms,망락회화주포솔화연시분별평균하강62.71%화73.88%.
With the continuous development of computer network and the enhancement of network performance requirements, it is difficult to meet the people's needs for traditional network. OpenFlow makes up for traditional network. However network session identification is inefficient and packet forwarding path selection is poor. Focusing on forwarding path and matching, this paper proposes GODP (GPU OpenFlow data processing) model. The GODP model combines GPU with biological sequence algorithms and machine learning methods, and presents the GPTWF network session matching algorithm and network session forwarding algorithm to accelerate matching speed and improve network environment. The experiments show that network session matching algorithm gives a speedup 290, and network session forwarding algorithm makes link loss rate less than 5%, with an average decline 62.71%, and network delay less than 20 ms, average decline 73.88%.