Profit-Aware Task Allocation in Satellite Computing

Jie Huang, Ruolin Xing, Xiao Ma, Ao Zhou, Shangguang Wang

Abstract: The rapid evolution of satellite networks promises to expand global internet service. However, optimizing task allocation for the efficient and sustainable operation of satellite computing presents complex challenges. Existing approaches usually prioritize energy considerations while neglecting economic aspects, which restricts satellite networks from achieving their full economic potential. In this paper, we address this gap by investigating task allocation in satellite computing. Our approach encourages satellites to consistently provide resources and optimizes battery usage, enabling the completion of more tasks and ultimately maximizing profit. The task allocation approach involves two key components: task pricing and task scheduling. Firstly, we introduce a unique task pricing algorithm that adheres to economic properties, establishing a direct link between satellite utilization and financial income, ensuring economically viable satellite operations. Moreover, we develop two distinct task scheduling algorithms tailored for offline and online scenarios, exploiting dynamic programming and reinforcement learning respectively. Extensive simulations demonstrate that our proposed algorithms effectively enhance task completion rates and optimize total satellite profit.