# Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks
## 2 Related Work and Motivation  
### Related Work  
In terrestrial networks, one of the technologies for solving the real-time transmission problem of network services is time-sensitive networking (TSN) proposed by the IEEE 802.1 working group [5]. TSN integrates technologies such as time synchronization, traffic shaping, traffic routing and scheduling, and preemption mechanisms. This not only enables real-time transmission of delay-sensitive services based on traditional Ethernet but also ensures compatibility with non-delay-sensitive services. Currently, TSN research mainly focuses on issues like protocol standards formulation [5], performance analysis [6], traffic routing and scheduling [7; 8; 9; 10], and time synchronization [11]. Although TSN network technology can provide deterministic delay guarantees in terrestrial networks, existing TSN network technologies cannot be directly applied to LEO satellite networks due to the variability of inter-satellite link distances. On the other hand, edge computing technology extends the cloud computing platform to network edge nodes, providing users with multi-layer and heterogeneous computing resources. As a promising paradigm, edge computing is considered a key enabler to further increase the transmission rate and reduce processing delay for integrated satellite-terrestrial networks [12; 13; 14]. One typical and novel work in [14] addressed the allocation of bandwidth and computation resources in low-Earth-orbit satellite networks for Earth observation applications. In this context, the satellites themselves process observation data to avoid transmitting useless data to ground stations and wasting precious bandwidth. To transfer the data from the data-gathering satellite to the satellite within the station's range,a routing path was needed. Both routing (determining the data's path) and computation resource allocation (deciding where the data should be processed--either on a satellite or on the ground) are interconnected challenges in this problem. Consequently, algorithms were developed to tackle these interconnected problems. However, the algorithms presented in [14] cannot be directly applied as a benchmark for our work because: (1) Our work focuses on the routing problem for a given set of continuous, infinite-time data transmission flow requests. In contrast, ref. [14] primarily considers routing design for Earth observation data, which typically involves periodic transmission; (2) In [14], the routing request only specifies the source satellite, while the destination is not predetermined and can be any ground station. The destination is determined simultaneously by computation resource allocation. Then, the emphasis of the routing is on arranging computation resources for applications and transmitting data to any ground station. Conversely, our work deals with predefined routing requests, and our concern is solely on fulfilling the routing requests from the source to the designated destination while guaranteeing QoS. In this paper, we mainly support delay-sensitive services in LEO satellite networks through traffic shaping techniques in the data plane (such as ATS) combined with routing optimization algorithms in the control plane. Currently, there are many related studies on traffic routing optimization in terrestrial networks [3; 4]. However, these methods cannot be directly applied to the rapidly changing topology of LEO satellite network scenarios through simple extensions. Although the topology structure of LEO satellite networks is dynamic, it also changes in a predictable manner [15; 16]. Therefore, based on the characteristics of the periodic changes in satellite network topology, the motion period \(T\) of a satellite network is divided into \(m\) time slots \(T_{1},T_{2},\ldots,T_{m}\), where the time slot \(T_{i}\) corresponds to a topology snapshot \(G_{i}\)[17; 18], which means that the topology snapshot \(G_{i}\) does not change during time slot \(T_{i}\).