[Paper] Resource-Aware Task Allocator Design: Insights and Recommendations for Distributed Satellite Constellations
Source: arXiv - 2601.06706v1
Overview
The paper introduces RATA (Resource‑Aware Task Allocator), a scheduler designed for distributed satellite constellations that can dynamically assign real‑time processing jobs across dozens to hundreds of LEO/MEO satellites. By jointly considering compute, storage, bandwidth, battery state, and orbital eclipses, the authors show how RATA can expose the performance limits of large‑scale satellite networks and pinpoint the exact resource that becomes the bottleneck under heavy workloads.
Key Contributions
- RATA design: a lightweight, telemetry‑driven allocator that continuously monitors arrival rates, on‑board resources, and eclipse windows to make placement decisions.
- SLTN‑based cooperative architecture: a single‑level tree network model that captures realistic inter‑satellite communication patterns for task hand‑off.
- Extensive empirical evaluation: simulations of tens of thousands of tasks across constellations ranging from a few satellites up to several hundred, under varying traffic intensities.
- Quantitative performance thresholds: identification of a “satellite‑count limit” where blocking probability and latency explode, while energy consumption stays relatively stable.
- Root‑cause analysis: demonstration that CPU availability—not power—is the primary factor driving task blocking in the examined scenarios.
Methodology
- Simulation environment – The authors built a discrete‑event simulator that models satellite orbits, eclipse periods, and the SLTN communication topology.
- Workload generation – Real‑time tasks are injected with Poisson arrival processes, spanning light (≈10 K tasks) to stress (≈100 K tasks) loads. Each task specifies CPU cycles, memory, and bandwidth needs.
- RATA logic – At each scheduling epoch, RATA reads telemetry (CPU load, remaining battery, link capacity) and selects a satellite that can finish the task before the next eclipse or hand‑off deadline. If no suitable node exists, the task is blocked.
- Metrics collected – Blocking probability, end‑to‑end response time, per‑satellite energy consumption, and overall resource utilization.
- Parameter sweep – Experiments vary constellation size (10–300 satellites), orbit altitude (LEO vs. Low‑MEO), and traffic intensity to map out scaling behavior.
Results & Findings
| Metric | Trend with Constellation Size | Key Insight |
|---|---|---|
| Blocking probability | Grows non‑linearly after ~150 satellites (sharp rise) | CPU slots become scarce; adding more nodes no longer helps. |
| Response time | Increases sharply once blocking spikes, indicating queuing buildup. | |
| Energy consumption | Remains flat or even improves under solar‑aware scheduling; eclipses cause only modest spikes. | |
| Resource utilization | CPU utilization hits ~90 % at the threshold, while memory and bandwidth stay under 60 %. |
The authors pinpoint a practical limit: for the baseline SLTN, ≈120–150 satellites is the sweet spot where capacity gains are still linear. Beyond that, the system transitions from graceful degradation to a collapse in QoS, driven primarily by insufficient processing cores rather than power constraints.
Practical Implications
- Constellation designers can use the identified thresholds to decide how many satellites to launch for a given workload class, avoiding over‑provisioning that yields diminishing returns.
- Edge‑computing services (e.g., on‑orbit AI inference, Earth‑observation data preprocessing) should prioritize CPU scaling (more cores, heterogeneous accelerators) over adding more satellites when aiming to reduce task blocking.
- Mission planners can integrate RATA‑style telemetry into ground‑segment schedulers to dynamically rebalance workloads during eclipse periods, extending mission life without extra hardware.
- Software developers building payload applications can expose resource‑usage hints (e.g., expected CPU cycles) to enable RATA to make smarter placement decisions, similar to container resource requests in Kubernetes.
- Energy budgeting becomes less of a concern for compute‑intensive missions, allowing designers to allocate more solar panel area to other subsystems (e.g., high‑gain antennas) without sacrificing task throughput.
Limitations & Future Work
- Simulation‑only validation – Real‑world on‑orbit experiments are needed to confirm the model’s assumptions about link latency and eclipse timing.
- Single‑level tree network – More complex mesh or hierarchical topologies could change the blocking dynamics; the authors suggest extending RATA to multi‑level routing.
- Homogeneous hardware model – Future work should explore heterogeneous compute (GPUs, FPGAs) and the impact of task off‑loading to ground stations.
- Security and fault tolerance – The current design does not address malicious task injection or satellite failures, which are critical for operational deployments.
Bottom line: RATA offers a concrete, telemetry‑driven framework for scaling distributed satellite processing pipelines, and its findings give developers and system architects a clear roadmap for balancing satellite count, compute resources, and workload intensity.
Authors
- Bharadwaj Veeravalli
Paper Information
- arXiv ID: 2601.06706v1
- Categories: cs.DC, cs.ET
- Published: January 10, 2026
- PDF: Download PDF