This paper presents a framework for integrating Low-Earth Orbit (LEO) platforms with Non-Terrestrial Networks (NTNs) in the emerging 6G communication landscape. Our work applies the Mega-Constellation Services in Space (MCSS) paradigm, leveraging LEO mega-constellations’ expansive coverage and capacity, designed initially for terrestrial devices, to serve platforms in lower LEO orbits. Results show that this approach overcomes the limitation of sporadic and time-bound satellite communication links, a challenge not fully resolved by available Ground Station Networks and Data Relay Systems. We contribute three key elements: (i) a detailed MCSS evaluation framework employing Monte Carlo simulations to assess space user links and distributions; (ii) a novel Space User Terminal (SUT) design optimized for MCSS, using different configurations and 5G New Radio Adaptive Coding and Modulation; (iii) extensive results demonstrating MCSS’s substantial improvement over existing Ground Station Networks and Data Relay Systems, motivating its role in the upcoming 6G NTNs. The space terminal, incorporating a multi-system, multi-orbit, and software-defined architecture, can handle Terabit-scale daily data volumes and minute-scale latencies. It offers a compact, power-efficient solution for properly integrating LEO platforms as space internet nodes.

One of the biggest advantages of cloud infrastructures is the elasticity. Cloud services are monitored and based on the resource utilization and performance load, they get scaled up or down, by provision or de-provision of cloud resources. The goal is to guarantee the customers an acceptable performance with a minimum of resources. Such Quality of Service (QoS) characteristics are stated in a contract, called Service Level Agreement (SLA) negotiated between customer and provider. The approach of this paper shows that with additional imprecise information (e.g. expected daytime/week- time performance) modeled with fuzzy logic and used in a behavior, load and performance prediction model, the up and down scaling mechanism of a cloud service can be optimized. Evaluation results confirm, that using this approach, SLA violation can be minimized.