Research on Edge Server Deployment Strategy in LEO Mega-Constellation

Abstract

The integration of Mobile Edge Computing (MEC) and Low Earth Orbit (LEO) satellite networks holds the potential to offer ubiquitous computing services for ground users and has garnered significant attention recently. While there has been extensive research conducted in the field of Satellite Mobile Edge Computing (SMEC), research on edge server deployment in mega-constellation is overlooked. Edge servers require an appropriate quantification and placement before the implementation of computation offloading. The improper deployment strategy can result in high access latency and imbalance workload. In this paper, we propose a two-stage approach, called cluster-based small-scale server deployment (CSSD), for small-scale placing and dynamic allocating edge servers that enable low access latency and workload balancing. Specifically, the offline stage is employed to determine the optimal placement of edge servers and the initial offloading mapping from access satellites to service satellites. The online stage, building dynamically adjusts the offloading mapping based on the spatiotemporal positions and workload of the satellites to balance system workload. Evaluation results show that CSSD outperforms other approaches with up to 16.37% and 35.38% enhancements in terms of workload standard deviation and user service rate while maintaining low access latency.

移动边缘计算（Mobile Edge Computing，MEC）与低地球轨道（Low Earth Orbit，LEO）卫星网络的融合为地面用户提供泛在计算服务展现出巨大潜力，近年来受到广泛关注。

尽管卫星移动边缘计算（Satellite Mobile Edge Computing，SMEC）领域已有大量研究，但巨型星座场景下的边缘服务器部署问题仍被忽视。边缘服务器需在实施计算 装配/安装(offload) 前完成合理的量化与部署，不当的部署策略将导致高访问时延和负载不均衡。

本文提出 基于聚类的小规模服务器部署（Cluster-based Small-scale Server Deployment，CSSD）两阶段方法 ，通过小规模部署与动态调度实现低时延访问和负载均衡。

具体而言：

1. 离线阶段：确定边缘服务器的 最优部署位置 及接入卫星与服务卫星间的 初始 装配/安装 映射关系
   • 基于遗传算法的启发式方法
2. 在线阶段：基于卫星时空位置与负载状态 动态调整 装配/安装 映射 以平衡系统负载
   • 基于卫星动态的数据信息进行“选择负载低（即使远距离）”的Server调度

实验表明，CSSD在保证低访问时延的同时，其负载标准差和用户服务率指标较现有方法分别提升达16.37%和35.38%