# A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing
## 2 Literature Review and Contribution  
### Related Works  
STN has been investigated as a promising solution to provide various cloud services and applications in next generation network architectures. [PERSON] et al. [2] deployed the computing resource in multi-layer heterogeneous edge computing clusters for STN, considering the Quality of Service (QoS) requirements, computation offloading, and task scheduling. [PERSON] et al. [3] designed a space edge computing node for STN to provide services coordinated with the terrestrial computing network, taking less computing time and consuming less energy than traditional satellite constellation. [PERSON] et al. [5] presented a three-tier computation architecture combining hybrid terrestrial cloud computing architecture and an edge computing Low Earth Orbit (LEO) satellite network to minimize the sum power consumption of the user-terminal. [PERSON] et al. [13] proposed a resource place method to select a subset of satellites as computing nodes with QoS constraints. A joint offloading decision and resource allocation scheme is designed in [14] considering satellites as the computing node to assist the terrestrial computing network in task processing, which minimizes the completion delay of computing tasks.  
In order to implement the STN in the above studies and provide a minimal delay for the user-terminal with QoS requirements, a powerful terrestrial computing network is necessary. The decomposed computing architecture has been considered as an emerging paradigm with flexible resource provision and faster task runtime. Several experimental studies have been conducted to investigate the feasibility of decomposed architectures.  
According to the analysis in [15], the low packet loss and long-term network stability are critical, but the extra network latency impacts the performance of decomposed architectures the most. Some decomposed architectures were proposed based on current servers and the hierarchical electrical network to implement decoupled memory blades [16; 17]. A decomposed in-memory store framework was proposed in [18] for big data applications, utilizing theThymesisFlow memory system. A Software-Defined Networking (SDN)-based orchestration plane is designed and experimentally implemented for a decomposed computing architecture in [19] with hybrid optical switches. Decomposed architectures based on optical switches were proposed and experimentally investigated in [20; 21], exploiting high aggregation bandwidths and transparent switching; however, the high switching delay (milliseconds) of the optical switch technology cannot provide nanoseconds scale communication between processor and memory cubes. An optically connected memory architecture was demonstrated in [22; 23] based on micro-ring resonator switches and the Aurora 64B/66B protocol, which achieved an optical switching time of hundreds of microseconds. To provide fast optical switching, a flat optical network was developed based on tunable lasers and passive gratings [24], which may lead to unstable operation during high-speed processing. A decomposed optical computing architecture was proposed based on Nanoseconds Optical Switches (NOS) [25]. The NOS is based on a broadcast and select switch architecture employing Semiconductor Optical Amplifiers (SOA)-based optical gates. Exploiting the nanoseconds switching of SOA-based gates and parallel optical flow control, the proposed architecture can potentially provide an optical network interconnection of high bandwidth and low latency for decomposed architectures.