# A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing
## 1 Introduction  
In order to satisfy various requirements of emerging big data applications (like Internet of Things (IoT), automatic driving, and high-quality video/image processing), a novel integrated Satellite-Terrestrial Network (STN) has been proposed combing the space-based satellite communication system and terrestrial computing network [1; 2; 3; 4]. The STN achieves stable global coverage to the user-terminal utilizing satellite networks, while relieving the limited processing performance of the satellite system by offloading the computing procedure to terrestrial computing infrastructures [5; 6]. The satellites can be applied as front-end nodes and process delay sensitive tasks with less requirements of computing performance. Meanwhile, the terrestrial network coordinates with satellites as the back-end to process computing intensive tasks. This architecture requires a powerful and efficient terrestrial computing network. However, because of the integrated on-board hardware in current server-centric terrestrial computing architecture, a large amount of hardware resources (processor, memory, and storage) is frequently underutilized [7]. Moreover, the application performance can be degraded under server-centric computing architectures when limited on-board hardware resources cannot meet the application requirements. In addition, the failure or upgrade of just one kind of resource (only processor or memory) in a server causes a significant impact to the whole server. To relieve the above issues of server-centric architectures, a novel computing architecture consisting of decomposed hardware cubes (hence, naming decomposed architecture) is developed to provide flexible resource provision and boosting performance for various processor (memory) intensive applications [8; 9; 10; 11; 12]. In order to implement the decomposed architecture, there are several issues to be addressed, in which the disaggregation of the processor and memory is a key challenge. Due to the hardware decomposition, the on-board high-speed point-to-point bus between the processor and memory is replaced by the network interconnection. This difference requires a network connection of high bandwidth and low latency to sustain the traffic among decomposed hardware cubes. The network switching may also lead to signal deterioration (including channel crosstalk, OSNR degradation, signal distortion, and power budget), hence, degrading the computing performance. Therefore, a stable, error-free, and low packet loss network is also significantly important for decomposed architectures. In this paper, we design and implement an optical terrestrial computing network with decomposed hardware for STN edge computing, while hardware cubes are interconnected by an NOS-based flat network to minimize the communication delay. An independent optical packet control plane is designed to process the optical packet switching and solve the packet contention. The decomposed computing prototype is experimentally implemented with a \(4\times 4\) NOS and four processor/memory cubes. The hardware resource cubes (processor and memory) are implemented based on Field-Programmable Gate Array (FPGA) platforms. The physical and network performance of the optical computing prototype with decomposed hardware are experimentally assessed, while the scalability of the decomposed computing architecture (up to 4096 hardware cubes) is also numerically investigated based on experimentally measured parameters. The article is organized as follows. Section 2 provides an overview of the existing works and describes the contributions of this work. In Section 3, the NOS-based decomposed optical computing architecture for STN and its operation are presented. In Section 4, the experimental demonstration of the NOS-based decomposed computing prototype is discussed. Section 5 shows the scalability of the NOS-based computing architecture with decomposed hardware in terms of physical performance and network performance.