关键词: OpenFlow技术, GODP模型, GPU计算, 生物序列, 机器学习

Abstract: 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%.  

Key words: OpenFlow technique, GODP model, GPU computation, biological sequence, machine learning