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.

Executing 5G core network functions on satellites has become crucial to enhance satellite network management and service capabilities. The User Plane Function (UPF) is responsible for efficient data traffic forwarding and is envisioned as a key and pioneering core network function that will be deployed on satellites. However, managing and providing services with satellite UPFs face dual challenges. Limited satellite resources constrain the user scale that a satellite UPF can service, resulting in an unguaranteed service delay. Moreover, the extremely rapid mobility of satellites renders it difficult for satellite UPFs to provide seamless services. To address the above challenges, this paper presents the first-of-its-kind service optimization scheme for satellite UPFs in terms of switch control, state migration, and traffic routing. To provide guaranteed service delay, we provide a theoretical analysis based on the M/G/1 queue model, demonstrating the service delay-resource consumption trade-off. A satellite UPF switch control scheme is integrated into the service optimization process, which can decrease satellite UPF service delay while saving satellite resources by adjusting the switch control parameters. To provide seamless services, we propose a satellite UPF-oriented state-aware service migration and traffic routing (UPF service optimization) algorithm. A policy network-based reinforcement learning approach is employed to dynamically perceive the satellite network’s state as well as the satellite UPF switch state. Building upon the optimization of service delay through satellite UPF switch control, the processes of state-aware state migration and traffic routing are further employed to reduce delay, ensuring seamless service effectively. Experiments reveal that the proposed algorithm outperforms other benchmark algorithms under different metrics. The service delay is reduced by an average of 23.2% compared with other algorithms.

Recent advancements in Low Earth Orbit (LEO) satellites are facilitating the provision of Deep Neural Networks (DNNs)-inherent services to achieve ubiquitous coverage via satellite computing. However, the computational demands and energy consumption of DNN models present significant challenges for satellite computing with limited power and computation resources. Based on the layered characteristics of DNN models, a satellite-ground co-inference strategy has been introduced, which executes certain layers on satellites and the remaining layers on ground servers. Determining the optimal layers for in-orbit processing, however, is non-trivial due to the under-explored energy consumption of satellite computing across different models and restricted yet varying communication conditions of satellite-ground links. In this paper, we first conduct a comprehensive measurement to uncover energy consumption of satellite computing across different layers and models. By summarizing the key observations, we develop a layer-specific energy consumption model tailored to diverse DNN architectures and kernels. We then investigate the energy-efficient satellite-ground co-inference problem and formulate it as an integer-nonlinear programming problem, which presents high computational complexity. To tackle these difficulties, we propose a satellite-ground co-inference algorithm that employs a branch-and-bound strategy, combined with the Sobol sequence and Lagrange multiplier, to reduce complexity and ensure stability across diverse DNN architectures. To evaluate the proposed algorithm, we conduct experiments based on real-world satellite parameters. The results demonstrate that our proposed algorithm can achieve an average energy savings of 96% under various data volumes compared to the existing benchmarks.

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.

With the rapid proliferation of large Low Earth Orbit (LEO) satellite constellations, a huge amount of in-orbit data is generated and needs to be transmitted to the ground for processing. However, traditional LEO satellite constellations, which downlink raw data to the ground, are significantly restricted in transmission capability. Orbital edge computing (OEC), which exploits the computation capacities of LEO satellites and processes the raw data in orbit, is envisioned as a promising solution to relieve the downlink burden. Yet, with OEC, the bottleneck is shifted to the inelastic computation capacities. The computational bottleneck arises from two primary challenges that existing satellite systems have not adequately addressed: the inability to process all captured images and the limited energy supply available for satellite operations. In this work, we seek to fully exploit the scarce satellite computation and communication resources to achieve satellite-ground collaboration and present a satellite-ground collaborative system named TargetFuse for onboard object detection. TargetFuse incorporates a combination of techniques to minimize detection errors under energy and bandwidth constraints. Extensive experiments show that TargetFuse can reduce detection errors by 3.4× on average, compared to onboard computing. TargetFuse achieves a 9.6× improvement in bandwidth efficiency compared to the vanilla baseline under the limited bandwidth budget constraint.

Recent advancements in Low Earth Orbit (LEO) satellites are facilitating the provision of Deep Neural Networks (DNNs)-inherent services to achieve ubiquitous coverage via satellite computing. However, the computational demands and energy consumption of DNN models pose significant challenges for satellite computing with limited power and computation resources. Based on the hierarchical characteristics of DNN models, we propose a satellite-ground co-inference strategy that executing certain layers on satellites and the remaining layers on ground servers. However, identifying the optimal layers for in-orbit processing with latency constraints is challenging due to the uncertain energy consumption across diverse models. To explore the correlation between energy consumption and layer types, we conduct comprehensive measurements on a hardware device commonly found in commercial LEO satellites and develop a layer-based energy consumption prediction model. Then, we formulate an optimization problem of minimizing the energy consumption on the satellite within the latency constraint as an integer nonlinear programming problem. Solving this problem is difficult due to combinatorial explosion in the discrete solution space. To address this, we propose an improved algorithm based on genetic algorithms. Using configurations from a real satellite, we conduct simulation experiments, concluding that our algorithm significantly improves energy savings by an average of 27 ×.

In the wake of the rapid deployment of large-scale low-Earth orbit satellite constellations, exploiting the full computing potential of Commercial Off-The-Shelf (COTS) devices in these environments has become a pressing issue. However, understanding this problem is far from straightforward due to the inherent differences between the terrestrial infrastructure and the satellite platform in space. In this paper, we take an important step towards closing this knowledge gap by presenting the first measurement study on the thermal control, power management, and performance of COTS computing devices on satellites. Our measurements reveal that the satellite platform and COTS computing devices significantly interplay in terms of the temperature and energy, forming the main constraints on satellite computing. Further, we analyze the critical factors that shape the characteristics of onboard COTS computing devices. We provide guidelines for future research on optimizing the use of such devices for computing purposes. Finally, we have released the datasets to facilitate further study in satellite computing.

Satellite computing is emerging as a promising domain for delivering mobile services that meet stringent Quality of Service (QoS) requirements, such as low latency, to users. However, the inherent mobility of satellites as computing nodes can precipitate QoS degradation, a challenge not encountered in terrestrial cloud systems. This discrepancy poses significant adaptation challenges for cloud service orchestration systems, such as Kubernetes, due to the rapid movement of satellites. This poster introduces a service orchestration system and a corresponding service placement strategy tailored for satellite computing environments. Our proposed architecture and strategy surpass traditional fixed instance deployment by not only achieving lower average latency but also maintaining an optimal balance between benefits and costs.

Small satellites (SmallSats) are now widely used in various fields, such as real-time communication and earth observation. These increasingly complex space applications face limited support from conventional radiation-hardened processors onboard. Hence, many SmallSats are designed to utilize high performance commercial off-the-shelf (COTS) computing devices to address this problem but it remains unclear how the unique energy and thermal characteristics of SmallSats impact computing efficiency onboard. This work conducts a systematic and quantitative measurement study of COTS devices’ computing efficiency on two real orbiting SmallSats. The key findings are: 1) inadequate energy management may lead to electricity wastage in sunlit zones and shortages in eclipse zones, impacting onboard computing availability and 2) the weak heat dissipation onboard may compromise COTS computing efficiency by incurring thermal throttling. To address such challenges, we design ProScale, a lightweight application-aware power management and thermal control system to improve computing efficiency under both electrical and thermal energy constraints. Evaluation shows that ProScale can improve the average task completion latency by $2.1 \times$ for computation-intensive applications compared with baselines.