Hurry: Dynamic Collaborative Framework For Low-orbit Mega-Constellation Data Downloading

TL;DR

This paper introduces Hurry, a dynamic collaborative framework that significantly improves satellite data download efficiency in low-orbit mega-constellations, outperforming existing methods like CoDld especially as constellation size grows.

Contribution

Hurry models satellite topology changes with Time-Expanded Graphs and implements a novel algorithm to prevent download degradation in large constellations.

Findings

Hurry achieves 100% download completion in fixed data volume scenarios.

Hurry improves throughput from 11% to 66% over CoDld in continuous data scenarios.

Hurry effectively mitigates exponential degradation in large satellite constellations.

Abstract

Low-orbit mega-constellation network, which utilize thousands of satellites to provide a variety of network services and collect a wide range of space information, is a rapidly growing field. Each satellite collects TB-level data daily, including delay-sensitive data used for crucial tasks, such as military surveillance, natural disaster monitoring, and weather forecasting. According to NASA's statement, these data need to be downloaded to the ground for processing within 3 to 5 hours. To reduce the time required for satellite data downloads, the state-of-the-art solution known as CoDld, which is only available for small constellations, uses an iterative method for cooperative downloads via inter-satellite links. However, in LMCN, the time required to download the same amount of data using CoDld will exponentially increase compared to downloading the same amount of data in a small constellation. We have identified and analyzed the reasons for this degradation phenomenon and propose a new satellite data download framework, named Hurry. By modeling and mapping satellite topology changes and data transmission to Time-Expanded Graphs, we implement our algorithm within the Hurry framework to avoid degradation effects. In the fixed data volume download evaluation, Hurry achieves 100% completion of the download task while the CoDld only reached 44% of download progress. In continuous data generation evaluation, the Hurry flow algorithm improves throughput from 11% to 66% compared to the CoDld in different scenarios.