The Construction of High-Performance Optoelectronic Interconnection Network and the Realization of Cloud Computing for Traffic Data Recognition

Abstract

Due to the problems of congestion, insufficient scalability, and high cost in the network core layer link, the network interconnection architecture needs to be optimized. The photoelectric interconnection technology has attracted the attention of many researchers with its high bandwidth, low latency, and low power consumption. In this study, after fully considering the requirements of optoelectronic switching on network node degree and processing delay and corresponding to different transmission characteristics of circuit switching and optical circuit switching, an optoelectronic interconnection architecture supporting heterogeneous network topology, namely THtop, is proposed. According to the optical circuit switching/circuit switching characteristics of the Internet, the corresponding topology structure is designed respectively in this architecture to enhance the system integration and reduce the cost. At the same time, in order to meet the requirements of wavelength and number of hops of optical circuit switching, distributed control is designed in THtop, and the design method of heterogeneous optical switching unit is introduced in the switching equipment, so that the optical switching node can further reduce the cost while playing its role. The use of high-speed optoelectronic networks will cause a large amount of traffic on the channel. In this study, the traffic identification is analyzed on the proposed THtop architecture. The Map Reduce framework in cloud computing is used as the identification model, and the K-means algorithm is taken as the identification algorithm. According to its characteristics, the W_K-means algorithm is proposed for the type identification of traffic. The results show that under the OpenNet Internet simulation environment, when the traffic is set to a certain level, more than 90% of the non-blocking network switching can be realized under the THtop architecture, and the completion time of the mouse flow and elephant flow can be significantly improved. Therefore, under the Map Reduce model, the proposed W_K -means algorithm can quickly identify different traffic types.