The ever-increasing demand for ubiquitous, continuous, and high-quality services poses a great challenge to the traditional terrestrial network. To mitigate this problem, the mobile-edge-computing-enhanced low earth orbit (LEO) satellite network, which provides both communication connectivity and on-board processing services, has emerged as an effective method. The main issue in LEO satellites includes finding the optimal locations to host network functions (NFs) and then making offloading decisions. In this article, we jointly consider the problem of service chain caching and computation offloading to minimize the overall cost, which consists of task latency and energy consumption. In particular, the collaboration among satellites, the network resource limitations, and the specific operation order of NFs in service chains are taken into account. Then, the problem is formulated and linearized as an integer linear programming model. Moreover, to accelerate the solution, we provide a greedy algorithm with cubic time complexity. Numerical investigations demonstrate the effectiveness of the proposed scheme, which can reduce the overall cost by around 20% compared to the nominal case where NFs are served in data centers.

翻译：随着泛在、连续且高质量服务需求的不断增长，传统地面网络面临巨大挑战。为缓解这一问题，融合移动边缘计算的低地球轨道（LEO）卫星网络作为一种有效手段应运而生，该网络既能提供通信连接又能提供星载处理服务。LEO卫星的核心问题包括确定网络功能（NF）的最优部署位置并制定卸载决策。本文联合考虑了服务链缓存与计算卸载问题，旨在最小化由任务时延和能耗构成的总成本。特别地，我们兼顾了卫星间协作、网络资源约束以及服务链中NF的执行顺序约束。随后，该问题被建模并线性化为一个整数线性规划模型。此外，为加速求解过程，我们提出了一种三次时间复杂度的贪婪算法。数值实验验证了所提方案的有效性：与NF在数据中心处理的基准方案相比，该方案可降低约20%的总成本。