We present the design of a Resource-Aware Task Allocator (RATA) and an empirical analysis in handling real-time tasks for processing on Distributed Satellite Systems (DSS). We consider task processing performance across low Earth orbit (LEO) to Low-Medium Earth Orbit (Low-MEO) constellation sizes, under varying traffic loads. Using Single-Level Tree Network(SLTN)-based cooperative task allocation architecture, we attempt to evaluate some key performance metrics - blocking probabilities, response times, energy consumption, and resource utilization across several tens of thousands of tasks per experiment. Our resource-conscious RATA monitors key parameters such as arrival rate, resources (on-board compute, storage, bandwidth, battery) availability, satellite eclipses' influence in processing and communications. This study is an important step towards analyzing the performance under lighter to stress inducing levels of compute intense workloads to test the ultimate performance limits under the combined influence of the above-mentioned factors. Results show pronounced non-linear scaling: while capacity increases with constellation size, blocking and delay grow rapidly, whereas energy remains resilient under solar-aware scheduling. The analysis identifies a practical satellite-count limit for baseline SLTNs and demonstrates that CPU availability, rather than energy, is the primary cause of blocking. These findings provide quantitative guidance by identifying thresholds at which system performance shifts from graceful degradation to collapse.

翻译：本文提出了一种资源感知型任务分配器（RATA）的设计，并对其在分布式卫星系统（DSS）中处理实时任务的性能进行了实证分析。我们考察了低地球轨道（LEO）到低中地球轨道（Low-MEO）星座规模在不同流量负载下的任务处理性能。基于单层树网络（SLTN）协同任务分配架构，我们试图评估若干关键性能指标——每次实验涉及数万个任务时的阻塞概率、响应时间、能耗和资源利用率。我们的资源敏感型RATA监控关键参数，如到达率、资源（星载计算、存储、带宽、电池）可用性、卫星蚀对处理和通信的影响。本研究是迈向性能分析的重要一步，旨在涵盖从较轻到密集的计算负载水平，以测试在上述因素综合影响下的最终性能极限。结果显示显著的非线性标度：虽然容量随星座规模增加，但阻塞和延迟增长迅速，而能量在太阳感知调度下保持韧性。分析确定了基线SLTN的实用卫星数量上限，并表明CPU可用性（而非能量）是阻塞的主要原因。这些发现通过识别系统性能从平缓退化转变为崩溃的阈值，提供了量化指导。