## 2 The Challenge of Cloud-Native INCCOM  
As mobile edge computing (MEC) and AI continue to evolve, computing power has become ubiquitous. Networks must now offer intelligent services that enable efficient collaboration between cloud, edge, and terminal computing power [14]. The upcoming 6G network is aimed at achieving seamless global coverage, spanning across land, sea, air, and space. It will also enable the establishment of a hierarchical network which will facilitate centralized and distributed collaboration. The network will support distributed edge autonomy and management, alongside unified and simpler protocols. The integration of computing and communication systems will enable seamless collaboration among cloud, edge, network, and computing power. Communication and computing elements will understand and collaborate with each other, thereby achieving real-time and precise discovery of available computing power, flexible and dynamic computing, as well as connection service scheduling. These advancements will offer ubiquitous services, optimizing the allocation of computing resources, and enhancing the utilization efficiency of communication and computing resources. At the same time, cloud-native architecture is becoming a prevailing trend in modern software architecture design patterns. The objective is to enhance the elasticity, reliability, and scalability of applications. Cloud-native architecture highlights container-based design, microservices, continuous delivery, and automated management in order to improve application performance and ease of management. There is an urgent need to design and implement a smart cloud-native network platform that integrates centralized and distributed architectures and blends communication and computing. The main challenges are as follows. The primary challenge is how to effectively control and utilize fragmented resources on demand, encompassing efficient scheduling of distributed computing resources, complete utilization of fragmented data silos, appropriate utilization of diverse communication methods, and effective consolidation of decentralized and heterogeneous models. Task scheduling, node management, and structure merging are essential aspects to consider when scheduling fragmented resources. Distributed frameworks such as Hadoop [15] and Spark [16] have attained maturity and are extensively used in fields like data processing and machine learning, providing the ability to manage and schedule computing power in distributed environments. The problems of data silos and model consolidation can be addressed by utilizing machine learning frameworks such as federated learning, transfer learning, and lifelong learning. The second challenge for INCCOM involves ensuring network determinacy and security. The primary distinction between determinate and non-determinate networks is the assurance of transmission time. Non-determinate networks may encounter issues like lost, delayed, or disordered data packets which can affect real-time applications. In contrast, determinate networks utilize techniques that predict the time of data packet transmission and guarantee the reliability and stability of real-time applications. In cluster networks experiencing real-time topology changes, data security, communication security, and model security are crucial considerations. Blockchain technology and federated learning have potential in enhancing network and data security, making them a vital aspect of network security. It is crucial to note that the deployment method of the core network plays a significant role in the performance of satellite-ground integrated communication systems in both the present and future stages. However, the orchestration of cloud-native network functions in the core network remains an unresolved issue, with very few works dedicated specifically to addressing this problem. In the context of the satellite-ground fusion network, ref. [17; 18; 19] discuss several novel core network deployment and management architectures. Ref.