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任务分配器（RATA）的设计，并对分布式卫星系统（DSS）中实时任务的处理进行了实证分析。我们考虑了从低地球轨道（LEO）到低中地球轨道（Low-MEO）不同星座规模下，在不同流量负载下的任务处理性能。采用基于单级树网络（SLTN）的协作式任务分配架构，我们尝试评估一些关键性能指标——每次实验处理数万个任务时的阻塞概率、响应时间、能耗以及资源利用率。我们提出的资源感知型RATA监控关键参数，如任务到达率、资源（星上计算、存储、带宽、电池）可用性，以及卫星星蚀对处理和通信的影响。本研究是分析从轻载到压力诱导水平的计算密集型工作负载下系统性能的重要一步，旨在测试在上述因素综合影响下的最终性能极限。结果显示存在显著的非线性缩放现象：虽然容量随星座规模增加而增加，但阻塞和延迟迅速增长，而在考虑太阳能的调度下，能耗则保持弹性。该分析确定了基线SLTN架构的实用卫星数量上限，并证明CPU可用性（而非能量）是导致阻塞的主要原因。这些发现通过识别系统性能从优雅降级转向崩溃的阈值，提供了定量指导。