Scholarlink

<p>With the rapid development of large-scale low Earth orbit (LEO) satellite constellations, cohesive clustered satellite (CCS) systems are considered critical spatial information infrastructures for augmenting space resource efficiency by aggregating the service capabilities of multiple satellite platforms to form an ultracohesive virtual satellite. This paper aims to fill the gaps identified in the literature by providing an extensive survey on communication and computing resources management, integrating insights from academia and the industry of CCS systems. It discusses the evolution and components of CCS systems and then introduces a native artificial intelligence architecture along with a hierarchical structure for resource management. Furthermore, it provides the emerging technologies and applications brought by CCS. On this basis, the modeling and metrics of resource management are established. Then, an exhaustive review of resource management methods encompassing both traditional and artificial intelligence algorithms is presented. Among these methods, knowledge-driven resource scheduling strategies are highlighted. Finally, some future directions of CCS systems are outlined.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A survey on communication and computing resources allocation and management for cohesive clustered satellites systems

  • 期刊论文

  • 发表日期：

    2025年8月15日

查看全文
  • Chen Zhang,
  • Xin Wan,
  • Wanjing Li,
  • Haojun Liu,
  • Dongming Bian,
  • Gengxin Zhang

摘要

With the rapid development of large-scale low Earth orbit (LEO) satellite constellations, cohesive clustered satellite (CCS) systems are considered critical spatial information infrastructures for augmenting space resource efficiency by aggregating the service capabilities of multiple satellite platforms to form an ultracohesive virtual satellite. This paper aims to fill the gaps identified in the literature by providing an extensive survey on communication and computing resources management, integrating insights from academia and the industry of CCS systems. It discusses the evolution and components of CCS systems and then introduces a native artificial intelligence architecture along with a hierarchical structure for resource management. Furthermore, it provides the emerging technologies and applications brought by CCS. On this basis, the modeling and metrics of resource management are established. Then, an exhaustive review of resource management methods encompassing both traditional and artificial intelligence algorithms is presented. Among these methods, knowledge-driven resource scheduling strategies are highlighted. Finally, some future directions of CCS systems are outlined.