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Multi-Tree Genetic Programming with Elite Recombination for dynamic task scheduling of satellite edge computing

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 166卷

作者： Zhang, Changzhen Yang, Jun Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

satellite edge computing (SEC) can provide task computation services to terrestrial users, particularly in areas lacking terrestrial network coverage. With the increasing frequency of computational demands from Internet of Things (IoT) devices and the limited and dynamic nature of computational resources in Low Earth Orbit (LEO) satellites, making effective real-time scheduling decisions in dynamic environments to ensure high task success rate is a critical challenge. In this work, we investigate the dynamic task scheduling of SEC based on Genetic Programming Hyper-Heuristic (GPHH). Firstly, anew problem model for the dynamic task scheduling of SEC is proposed with the objective of improving the task success rate, where the real-world situations (limited and dynamic nature of satellite resources, randomness and difference of tasks) are taken into account. Secondly, to make efficient real-time routing decision and queuing decision during the dynamic scheduling process, a novel scheduling heuristic with routing rule and queuing rule is developed, considering dynamic features of the SEC system such as real-time load, energy consumption, and remaining deadlines. Thirdly, to automatically learn both routing rule and queuing rule, and improve the performance of the algorithm, a Multi-Tree Genetic Programming with Elite Recombination (MTGPER) is proposed, which exploits the recombination of the excellent rules to obtain the better scheduling heuristics. The experimental results show that the proposed MTGPER significantly outperforms existing state-of-the-art methods. The scheduling heuristic evolved by MTGPER has quite good interpretability, which facilitates scheduling management in engineering practice.

关键词： satellite edge computing Dynamic task scheduling Genetic programming Task success rate Elite recombination

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Deep Multiagent Reinforcement Learning for Task Offloading and Resource Allocation in satellite edge computing

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 3832-3845页

作者： Jia, Min Zhang, Liang Wu, Jian Guo, Qing Zhang, Guowei Gu, Xuemai Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Qufu Normal Univ Sch Cyber Sci Engn Jining 273165 Peoples R China

As a supplement to terrestrial communication networks, satellite edge computing can break through geographical limitations and provide on-orbit computing services for people in some remote areas to achieve truly seamless global coverage. Considering time-varying channels, queue delays, and dynamic loads of edge computing satellites, we propose a multiagent task offloading and resource allocation (MATORA) algorithm with weighted latency as the optimization goal. It is a mixed integer nonlinear problem decoupled into task offloading and resource allocation subproblems. For the offloading subproblem, we propose a distributed multiagent deep reinforcement learning algorithm, and each agent generates its own offloading decision without knowing the prior knowledge of others. We show that the resource allocation problem is convex and can be solved using convex optimization methods. The experiment shows that the proposed algorithm can better adapt to the change of channel and the dynamic load of edge computing satellite, and it can effectively reduce task latency and task drop rate.

关键词： Deep reinforcement learning multiagent resource allocation satellite edge computing task offloading Deep reinforcement learning multiagent resource allocation satellite edge computing task offloading

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Learning-Based Deadlock-Free Multi-Objective Task Offloading in satellite edge computing With Data-Dependent Constraints and Limited Buffers

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第1期12卷 356-368页

作者： Zhang, Ruipeng Feng, Yanxiang Yang, Yikang Li, Xiaoling Li, Hengnian Xi An Jiao Tong Univ Syst Engn Inst Sch Automat Sci & Engn Xian 710049 Peoples R China Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China Xian Satellite Control Ctr State Key Lab Astronaut Dynam Xian 710043 Peoples R China

satellite edge computing (SEC) is important for future network deployments because of its global coverage and low-latency computing services. Nevertheless, due to data dependencies among tasks and limited buffers in satellites, a coupling exists between transmission and computation, and undesired deadlocks may arise. This paper addresses task offloading in SEC and aims to minimize service latency, energy consumption, and time window violations simultaneously. First, a mixed-integer nonlinear programming model is presented. To resolve potential deadlocks, a deadlock amending algorithm (DAA) based on Petri net with polynomial time complexity is proposed. Deadlocks in solutions are amended by finding a transition sequence that corresponding transmission and computation can be performed sequentially. By embedding DAA, we develop a learning-based deadlock-free multi-objective scheduling algorithm (LDMOSA) that combines the exploration of evolutionary algorithms with the perception of reinforcement learning. To enhance the convergence and diversity of solutions, an initialization strategy employing problem-specific constructive heuristics is designed. Then, a learning-based mechanism is used to leverage real-time information to perform adaptive operator selection during the search process. Finally, extensive experiments demonstrate the effectiveness of DAA in resolving deadlocks, and the LDMOSA outperforms state-of-the-art algorithms for task offloading in SEC.

关键词： satellites System recovery Internet of Things Couplings Petri nets Scheduling algorithms Energy resolution Costs Vectors Traffic control satellite-terrestrial integrated networks satellite edge computing computation offloading multi-task offloading

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Energy-Constrained satellite edge computing for satellite-Terrestrial Integrated Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 3359-3374页

作者： Cheng, Lei Feng, Gang Sun, Yao Qin, Shuang Wang, Feng Quek, Tony Q. S. Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Scotland Singapore Univ Technol & Design Informat Syst Technol & Design Pillar Singapore 487372 Singapore

satellite edge computing (SEC) has emerged as an innovative paradigm for future satellite-terrestrial integrated networks (STINs), expanding computation services by sinking computing capabilities into Low-Earth-Orbit (LEO) satellites. However, the mobility of LEO satellites poses two key challenges to SEC: 1) constrained onboard computing and transmission capabilities caused by limited and dynamic energy supply, and 2) stochastic task arrivals within the satellites' coverage and time-varying channel conditions. To tackle these issues, it is imperative to design an optimal SEC offloading strategy that effectively exploits the available energy of LEO satellites to fulfill competing task demands for SEC. In this paper, we propose a dynamic offloading strategy (DOS) with the aim to minimize the overall completion time of arriving tasks in an SEC-assisted STIN, subject to the long-term energy constraints of the LEO satellite. Leveraging Lyapunov optimization theory, we first convert the original long-term stochastic problem into multiple deterministic one-slot problems parameterized by current system states. Then we use sub-problem decomposition to jointly optimize the task offloading, computing, and communication resource allocation strategies. We theoretically prove that DOS achieves near-optimal performance. Numerical results demonstrate that DOS significantly outperforms the other four baseline approaches in terms of task completion time and dropping rate.

关键词： satellites Low earth orbit satellites Optimization Resource management Vehicle dynamics Stochastic processes edge computing Convex functions Computational modeling Space-air-ground integrated networks Computation offloading satellite edge computing satellite-terrestrial integrated network

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SEC-DT: satellite edge computing Enabled Dynamic Data Transmission Based on GNN-Assisted MARL for Earth Observation Missions

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2025年 6卷 288-301页

作者： Xiao, Yuyang Zhai, Zhiwei Yu, Shuai Xu, Zhenlong Li, Lin Zhang, Fei Cao, Lu Chen, Xu Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China China Acad Space Technol Shandong Inst Space Elect Technol Yantai 264003 Peoples R China Acad Mil Sci Unmanned Syst Res Ctr Natl Inst Def Technol Innovat Beijing 100071 Peoples R China

Recent advancements in low Earth orbit (LEO) satellite technology have facilitated a substantial increase in the number of Earth observation (EO) satellites launched. However, transmitting voluminous imagery generated by these EO satellites to the ground still faces the challenges of limited satellite resources and dynamic satellite networks. To address this problem, we propose SEC-DT, a satellite edge computing (SEC) enabled computation-aware dynamic Data Transmission framework for jointly optimizing the routing selection and in-orbit imagery compression adoption. Specifically, we formulate an online optimization problem for concurrently delivering data from multiple EO satellites in a single EO mission, aiming to minimize the overall transmission and computation latency while ensuring the decent quality of the final downloaded data. Then we cast the problem as a partially observable Markov decision process and adopt an augmented multi-agent reinforcement learning (MARL) algorithm to solve this intractable online decision problem. Considering the natural graph structure of the satellite network, we innovatively integrate the graph neural network (GNN) into the MARL algorithm to form a GNN-assisted MARL framework, wherein GNN can capture the enriched semantic information present in satellite topology to achieve the fusion of diverse environmental states, which is beneficial for improving the decision-making effectiveness of agents. Finally, we conduct extensive experiments and ablation studies in various settings based on real-world datasets of StarLink and SkySat constellations. The experimental results have demonstrated the scalability and excellent performance of our algorithm compared with other baseline schemes.

关键词： satellites Routing Low earth orbit satellites Image coding Streams Topology Optimization Earth Space vehicles satellite constellations satellite edge computing routing in-orbit compression multi-agent reinforcement learning graph neural network

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satellite edge computing With Collaborative Computation Offloading: An Intelligent Deep Deterministic Policy Gradient Approach

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IEEE INTERNET OF THINGS JOURNAL 2023年第10期10卷 9092-9107页

作者： Zhang, Hangyu Liu, Rongke Kaushik, Aryan Gao, Xiangqiang Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Shenzhen Inst Beihang Univ Shenzhen 518038 Peoples R China Univ Sussex Sch Engn & Informat Brighton BN1 9RH England China Acad Space Technol Xian 710100 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

Enabling a satellite network with edge computing capabilities can complement the advantages further of a single terrestrial network and provide users with a full range of computing service. satellite edge computing is a potentially indispensable technology for future satellite-terrestrial integrated networks. In this article, a three-tier edge computing architecture consisting of the terminal-satellite-cloud is proposed, where tasks can be processed at three planes and intersatellites can cooperate to achieve on-board load balancing. Facing varying and random task queues with different service requirements, we formulate the objective problem of minimizing the system energy consumption under the delay and resource constraints, and jointly optimize the offloading decision, communication, and computing resource allocation variables. Moreover, the distribution of resources is based on the reservation mechanism to ensure the stability of the satellite-terrestrial link and the reliability of computation process. To adapt to the dynamic environment, we propose an intelligent computation offloading scheme based on the deep deterministic policy gradient (DDPG) algorithm, which consists of several different deep neural networks (DNNs) to output both discrete and continuous variables. Additionally, by setting the selection process of legal actions, the simultaneous decisions on offloading locations and allocating resources under multitask concurrency is realized. The simulation results show that the proposed scheme can effectively reduce the total energy consumption of the system by ensuring that the task is completed on demand, and outperform the benchmark algorithms.

关键词： satellites Task analysis edge computing Resource management Optimization Internet of Things Heuristic algorithms Computation offloading deep deterministic policy gradient (DDPG) intersatellite cooperative computing resource allocation satellite edge computing

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Battery-Aware Energy Optimization for satellite edge computing

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IEEE TRANSACTIONS ON SERVICES computing 2024年第2期17卷 437-451页

作者： Li, Qing Wang, Shangguang Ma, Xiao Zhou, Ao Wang, Yue Huang, Gang Liu, Xuanzhe Peking Univ Sch Comp Sci Beijing 100871 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Fujian Ocean Sky Silkroad Satellite Technol Co Ltd Beijing 100007 Peoples R China

satellite edge computing can incur dramatically increased energy demand onboard, which is met by satellite batteries during eclipses. Excessive energy usage during regular operations accelerates battery wear. Therefore, it is important and timely to optimize the energy consumption onboard to extend satellite batteries life. This article investigates battery-aware energy optimization for satellite edge computing under energy harvesting dynamics and wireless environment uncertainty. Inspired by the periodical energy harvesting and satellite-ground connection, we develop a pattern-aware online energy scheduling algorithm within an online convex optimization framework. This learning algorithm achieves theoretical guarantees of no regret and gradually zeroing constraint violations. We further exploit inter-satellites collaboration to extend the average battery life in a whole constellation where satellites have different battery capacity degradation. Trace-driven simulations show that our algorithm can significantly extend the battery life by 1.32x and effectively adapt to the energy harvesting dynamics and wireless environment uncertainty.

关键词： satellites Batteries Energy harvesting edge computing Orbits Optimization Wireless communication satellite edge computing energy optimization battery-awareness online convex optimization

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Dynamic Delay-Sensitive Observation-Data-Processing Task Offloading for satellite edge computing: A Fully-Decentralized Approach

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REMOTE SENSING 2024年第12期16卷 2184-2184页

作者： Zhang, Ruipeng Feng, Yanxiang Yang, Yikang Li, Xiaoling Li, Hengnian Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Changan Univ Sch Elect & Control Engn Xian 710049 Peoples R China China Xian Satellite Control Ctr State Key Lab Astronaut Dynam Xian 710043 Peoples R China

satellite edge computing (SEC) plays an increasing role in earth observation, due to its global coverage and low-latency computing service. In SEC, it is pivotal to offload diverse observation-data-processing tasks to the appropriate satellites. Nevertheless, due to the sparse intersatellite link (ISL) connections, it is hard to gather complete information from all satellites. Moreover, the dynamic arriving tasks will also influence the obtained offloading assignment. Therefore, one daunting challenge in SEC is achieving optimal offloading assignments with consideration of the dynamic delay-sensitive tasks. In this paper, we formulate task offloading in SEC with delay-sensitive tasks as a mixed-integer linear programming problem, aiming to minimize the weighted sum of deadline violations and energy consumption. Due to the limited ISLs, we propose a fully-decentralized method, called the PI-based task offloading (PITO) algorithm. The PITO operates on each satellite in parallel and only relies on local communication via ISLs. Tasks can be directly offloaded on board without depending on any central server. To further handle the dynamic arriving tasks, we propose a re-offloading mechanism based on the match-up strategy, which reduces the tasks involved and avoids unnecessary insertion attempts by pruning. Finally, extensive experiments demonstrate that PITO outperforms state-of-the-art algorithms when solving task offloading in SEC, and the proposed re-offloading mechanism is significantly more efficient than existing methods.

关键词： satellite edge computing earth observation satellite task offloading mobile edge computing

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Joint Service Deployment and Task Scheduling for satellite edge computing: A Two-Timescale Hierarchical Approach

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第5期42卷 1063-1079页

作者： Tang, Qinqin Xie, Renchao Fang, Zeru Huang, Tao Chen, Tianjiao Zhang, Ran Yu, F. Richard Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China China Mobile Res Inst Beijing 100053 Peoples R China Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518107 Peoples R China Carleton Univ Sch Informat Technol Ottawa ON K1S 5B6 Canada

In this paper, we establish a two-timescale framework for the joint service deployment and task scheduling problem in satellite edge computing networks. We aim to optimize the computing performance of networks with diverse quality-of-service (QoS) guarantees for computing tasks. Specifically, to capture the small-timescale network dynamics and task randomness, we formulate the task scheduling problem as a constrained Markov decision process (CMDP) to minimize the energy consumption, load imbalance and packet loss of networks while ensuring the long-term delay. The Lyapunov technique is employed to deal with the delay constraints. A soft actor-critic (SAC)-based deep reinforcement learning (DRL) framework is designed to learn the stationary scheduling policy. We further explore the significant impact of deploying diverse services on the performance of task scheduling in satellite edge computing. Considering that frequent deployment of services will incur huge deployment overhead, we optimize the service deployment on a larger timescale. The optimization problem is modeled as an integer programming problem to improve the service capability of networks and reduce service deployment costs. A heuristic-based atomic orbital search (AOS) approach is proposed to obtain the superior policy with low complexity. Due to the correlation between the problems of two timescales, a hierarchical solution is constructed to iteratively find the excellent solution. Finally, extensive simulations are conducted to validate the effectiveness and superiority of the proposed scheme.

关键词： satellite edge computing task scheduling service deployment two-timescale hierarchical framework

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An Energy-Efficient Collaborative Offloading Scheme With Heterogeneous Tasks for satellite edge computing

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第6期11卷 6396-6407页

作者： Zhang, Changzhen Yang, Jun Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

satellite edge computing (SEC) can offer task computing services to ground users, particularly in areas lacking terrestrial network coverage. Nevertheless, given the limited energy of low earth orbit (LEO) satellites, they cannot be used to process numerous computational tasks. Furthermore, most existing task offloading methods are designed for homogeneous tasks, which obviously cannot meet service requirements of various computational tasks. In this work, we investigate energy-efficient collaborative offloading scheme with heterogeneous tasks for SEC to save energy and improve efficiency. Firstly, by dividing computational tasks into delay-sensitive (DS) and delay-tolerant (DT) tasks, we propose a collaborative service architecture with ground edge, satellite edge, and cloud, where specific task offloading schemes are given for both sparse and dense user scenarios to reduce the energy consumption of LEO satellites. Secondly, to reduce the delay and failure rate of DS tasks, we propose an access threshold strategy for DS tasks to control the queue length and facilitate load balancing among multiple computing platforms. Thirdly, to evaluate the proposed offloading scheme, we develop the continuous-time Markov chain (CTMC) to model the traffic load on computing platforms, and the stationary distribution is solved employing the matrix-geometric method. Finally, numerical results for SEC are presented to validate the effectiveness of the proposed offloading scheme.

关键词： satellites Low earth orbit satellites Servers Delays edge computing Collaboration Energy consumption Computer architecture Real-time systems Internet of Things satellite edge computing offloading scheme energy-efficient Markov chain heterogeneous tasks

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