Collaborative Satellite Computing through Adaptive DNN Task Splitting and Offloading

TL;DR

This paper presents a novel collaborative satellite computing system that adaptively splits and offloads DNN tasks across satellites, improving efficiency and resource utilization in satellite networks.

Contribution

It introduces a workload-balanced adaptive task splitting scheme and a GA-based offloading method for efficient DNN inference in satellite networks.

Findings

Improved task completion rate over existing methods

Reduced delay in DNN task processing

Enhanced resource utilization in satellite networks

Abstract

Satellite computing has emerged as a promising technology for next-generation wireless networks. This innovative technology provides data processing capabilities, which facilitates the widespread implementation of artificial intelligence (AI)-based applications, especially for image processing tasks involving deep neural network (DNN). With the limited computing resources of an individual satellite, independently handling DNN tasks generated by diverse user equipments (UEs) becomes a significant challenge. One viable solution is dividing a DNN task into multiple subtasks and subsequently distributing them across multiple satellites for collaborative computing. However, it is challenging to partition DNN appropriately and allocate subtasks into suitable satellites while ensuring load balancing. To this end, we propose a collaborative satellite computing system designed to improve task processing efficiency in satellite networks. Based on this system, a workload-balanced adaptive task splitting scheme is developed to equitably distribute the workload of DNN slices for collaborative inference, consequently enhancing the utilization of satellite computing resources. Additionally, a self-adaptive task offloading scheme based on a genetic algorithm (GA) is introduced to determine optimal offloading decisions within dynamic network environments. The numerical results illustrate that our proposal can outperform comparable methods in terms of task completion rate, delay, and resource utilization.