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Temperature- and Energy-Aware Dynamic Task Scheduling and Computing Resource Allocation for Satellite Computing
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DOI：10.1109/TMC.2025.3637314.png)
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Abstract
En 中文
Satellite computing, as an emerging edge computing paradigm, extends computing and networking services into space. Due to the internal design constraints of low-Earth orbit (LEO) satellites and the challenges posed by the external environment, satellite computing faces inherent limitations, including severely constrained resources, non-rechargeable batteries, poor heat dissipation, and highly dynamic operating conditions, leading to unreliable and unsustainable quality of service. To address the above challenges and fully realize the potential of satellite computing, this paper investigates temperature- and energy-aware dynamic task scheduling and computing resource allocation, aiming to optimize service latency, reduce onboard energy consumption, and enhance operational profit. Solving this problem requires coordinating task scheduling and resource allocation, balancing communication and computation latency, and addressing the challenge of a vast search space. To solve the above challenges, we first formulate this problem as a repeated Stackelberg game by developing temperature and energy models. Through theoretical analysis, we show that this game leads to a convex optimization framework that exhibits exponential complexity. To accelerate the search for the Stackelberg equilibrium solution, we propose a dynamic task scheduling algorithm based on the interior point method, which reduces the computational complexity to polynomial order. Trace-driven simulations demonstrate that the proposed algorithm reduces task scheduling latency by 28.4% and improves utility by 13% on average.
Keywords:
Satellite computing
dynamic task scheduling
computing resource allocation
repeated Stackelberg game
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