# Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks
## 1 Introduction
Third, the propagation delay of the service path should meet the service's requirements for delay. For the first requirement, we can achieve it through efficient traffic shaping techniques such as the asynchronous traffic shaper (ATS) [2]. For the second and third requirements, we can develop network dynamic traffic routing optimization schemes based on software-defined networking (SDN) technology on the network control plane. Therefore, this paper mainly studies how to combine the above two methods to effectively support delay-sensitive services in LEO satellite networks. However, achieving efficient dynamic traffic routing optimization in LEO satellite networks still presents significant challenges. The reasons are as follows. 1. Rapid and dynamic changes in network topology: LEO satellites move rapidly and periodically in orbit, which leads to rapid changes in network topology. This may result in frequent reconfigurations of service routes, introducing significant reconfiguration overhead. Therefore, the service routing problem needs to consider how to minimize the number of path handovers while satisfying delay requirements. 2. Large network scale: The plans of various countries indicate that future LEO satellite networks will have thousands or even tens of thousands of satellites. Dynamic service routing in such a large-scale network requires algorithms with low complexity. 3. Unbalance traffic distribution: The distribution of usersin LEO networks is uneven, which results in unbalance traffic distribution in LEO satellite networks. For example, densely populated areas have higher traffic load, while sparsely populated areas have lower traffic load. Therefore, traffic routing optimization algorithms need to have better load balancing performance. In traditional networks, there has been a lot of research on network traffic optimization problems [3; 4], and many traffic optimization algorithms have been proposed. However, due to the unique characteristics of LEO satellite networks, these algorithms cannot be applied to LEO satellite networks. Therefore, based on the characteristics of LEO satellite networks, this paper studies the routing optimization problem of delay-sensitive services in LEO satellite networks. The main contributions of this paper are as follows:  
1. This paper first presents the LEO satellite network model and the traffic routing optimization requirements for delay-sensitive service and then models the traffic routing optimization problem or delay-sensitive services in LEO satellite networks as an integer linear programming (ILP) problem based on the network model and service requirements;
2. Since the traffic routing optimization problem for delay-sensitive service in LEO satellite networks is an NP-hard problem, we propose an efficient approximation algorithm to solve this problem. The algorithm can calculate a routing sequence that meets the requirements of each delay-sensitive service. Specifically, the proposed algorithm has the following characteristics: (1) it can satisfy the delay constraints of delay-sensitive service; (2) the number of paths in the computed routing sequence is small, which can reduce the number of path handovers; (3) it can achieve network-wide traffic load balancing; (4) it has low complexity and can be applied to large-scale network scenarios; and (5) it has good optimization performance in terms of routing handovers and network load balancing;
3. We develop an LEO satellite network evaluation testbed based on OpenVSwitch (OVS) and the open-network operating system (ONOS) controller to evaluate our proposed delay-sensitive service provision solution. The rest of this paper is organized as follows. Section 2 introduces the related work and motivations. Section 3 presents the network model and problem formulation. We introduce the proposed approximation algorithm for the routing optimization problem of delay-sensitive services in Section 4. The experimental testbed and results are described in Sections 5 and 6. We conclude the paper in Section 7.