# Design of Optical Tunnel Switching Networks for Big Data Applications
###### Abstract  
In this paper, we proposed large-scale optical tunnel switching networks based on the Torus topology network with WSS (Wavelength Selective Switch) for future big data applications. All nodes of the large-scale optical tunnel switching networks use WSS switch modules, and the communications between nodes use multiple \(\lambda\)s (wavelengths), where a tunnel is established with a wavelength which can be reused. The widely used MEMS (Micro-Electro-Mechanical Systems) and LCoS (Liquid Crystal on Silicon) technologies are all millisecond-level switching speeds, so the frame size of the optical frame switch is very large, and this will reduce switching performance. Therefore, they are only suitable for optical tunnel switching networks design, but are not suitable for optical frame switch design. This multi-plane Torus topology network architecture not only increases network throughput, but also has fault tolerance to increase network reliability. When the traffic is changed, the number of tunnels between nodes can be scheduled in time to balance the load traffic and avoid traffic loss. Therefore, it can not only schedule the number of tunnels in time to balance the load traffic, in order to avoid traffic loss, but also because the channel is fixedly established, this does not generate any buffer delay, and this because of the transmission using optical transmission unlimited speed, so it is a good choice for future big data applications that require high speed, high bandwidth and low latency. wavelength selective switch; torus topology network; optical tunnel switching network 202020  
and returning home is still a time-consuming shortcoming of the herd effect. All-round applications, such as smart retail, smart access control and smart city, will create the most intelligent and convenient artificial intelligence Internet of Things (AIoT) life experience. FaceMe's AI face recognition engine has a correct recognition rate of 98.41% in its ultra-high precision mode. FaceMe has the advantage of edge computing integration. The AIoT device can transfer data processing from the cloud to the edge computing, without relying on the cloud server, greatly improving the speed of face recognition. The rapid growth of the Internet, mobile networks and cloud services, such as 3D games, VR (Virtual Reality), AR (Augmented Reality) and cloud services, will increase the bandwidth requirements, and network latency issues are also more demanding. In addition, there is a smart controller that can control smart appliances and devices, including electric cookers, sweeping robots, smart desk lamps, air purifiers, smart sockets, artificial intelligence Internet of Things (AIoT), and so on. These new applications which yield unprecedented volumes of exchanged data have been driving the scale of data centers and the diversity of services. Current data center networks are unable to meet demand due to their insufficient performance, such as poor flexibility, high latency, lack of scalability and serious power consumption. In recent years, various optical switching systems have been developed and demonstrated, making them the best choice for next-generation data center and data center networking technologies [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. Test results show that compared with the electric spine-leaf data center network, OPTUNS achieves 82.6% power savings under high traffic load (locality), and significantly reduces the average (p99) delay [11]. The Torus topology data center network, using hybrid OPS/OCS (Optical Packet Switch/ Optical Circuit Switch) technology, proposes an energy-efficient, low-latency, data center internal network [8]. The optical frame switch has been discussed [12]. After the simulation, the \(10\times 10\) Torus optical frame switch requires about 24 \(\lambda\)s per link. One node has four links, so the total of 96 \(\lambda\)s is required to reach a throughput of nearly 100%. However, optical frame switches require faster optical switching times.