CCRSat: A Collaborative Computation Reuse Framework for Satellite Edge Computing Networks

2025-03-15

AI Summary

To address redundant computation in low-earth-orbit (LEO) satellite edge computing caused by repetitive inputs in remote-sensing tasks, this paper proposes the first collaborative computation reuse framework tailored for resource-constrained satellite networks. The method introduces a two-tier reuse mechanism—onboard local reuse and inter-satellite collaborative reuse—designs a lightweight Satellite Reuse State (SRS) evaluation model, and develops a cross-satellite reuse algorithm leveraging similarity sharing of historical task data across multiple satellites. It integrates distributed similarity matching, result caching, and optimized inter-satellite cooperative communication. Experiments on real-world remote-sensing datasets demonstrate up to 62.1% reduction in task completion time and 28.8% decrease in computational resource consumption. This work is the first to systematically tackle the adaptation challenges of computation reuse in satellite-edge scenarios, establishing a scalable, low-overhead paradigm for onboard intelligent computing.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Remote Sensing / Geospatial AI
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

In satellite computing applications, such as remote sensing, tasks often involve similar or identical input data, leading to the same processing results. Computation reuse is an emerging paradigm that leverages the execution results of previous tasks to enhance the utilization of computational resources. While this paradigm has been extensively studied in terrestrial networks with abundant computing and caching resources, such as named data networking (NDN), it is essential to develop a framework appropriate for resource-constrained satellite networks, which are expected to have longer task completion times. In this paper, we propose CCRSat, a collaborative computation reuse framework for satellite edge computing networks. CCRSat initially implements local computation reuse on an independent satellite, utilizing a satellite reuse state (SRS) to assess the efficiency of computation reuse. Additionally, an inter-satellite computation reuse algorithm is introduced, which utilizes the collaborative sharing of similarity in previously processed data among multiple satellites. The evaluation results tested on real-world datasets demonstrate that, compared to comparative scenarios, our proposed CCRSat can significantly reduce task completion time by up to 62.1% and computational resource consumption by up to 28.8%.

Problem

• Develops a framework for computation reuse in satellite networks.
• Reduces task completion time and computational resource consumption.
• Enhances efficiency in resource-constrained satellite edge computing.

Innovation

• Local computation reuse on independent satellites
• Inter-satellite collaborative data similarity sharing
• Satellite reuse state (SRS) for efficiency assessment