Learning-Based Matching Game for Task Scheduling and Resource Collaboration in Intent-Driven Task-Oriented Networks

Engineering ›› 2025, Vol. 54 ›› Issue (11) : 143 -154. DOI: 10.1016/j.eng.2025.07.033

Article

Author information +

History +

Received	Accepted	Published
2024-12-31	2025-07-30	2025-08-11
Issue Date	Revised Date
2025-08-11	2025-07-07

PDF (2248KB)

Abstract

With the rapid advancement of satellite communication technologies, space information networks (SINs) have become essential infrastructure for complex service delivery and cross-domain task coordination, facilitating the transition toward an intent-driven task-oriented coordination paradigm across the space, ground, and user segments. This study presents a novel intent-driven task-oriented network (IDTN) framework to address task scheduling and resource allocation challenges in SINs. The scheduling problem is formulated as a three-sided matching game that incorporates the preference attributes of entities across all network segments. To manage the variability of random task arrivals and dynamic resources, a context–aware linear upper-confidence-bound online learning mechanism is integrated to reduce decision-making uncertainty. Simulation results demonstrate the effectiveness of the proposed IDTN framework. Compared with conventional baseline methods, the framework achieves significant performance improvements, including a 4.4%–28.9% increase in average system reward, a 6.2%–34.5% improvement in resource utilization, and a 5.6%–35.7% enhancement in user satisfaction. The proposed framework is expected to facilitate the integration and orchestration of space-based platforms.

Keywords

Intent-driven network / Matching game / Resource allocation / Space information network / Task scheduling

Cite this article

Download citation ▾

Jiaorui Huang, Min Cao, Chungang Yang, Zhu Han, Tong Li. Learning-Based Matching Game for Task Scheduling and Resource Collaboration in Intent-Driven Task-Oriented Networks. Engineering, 2025, 54(11): 143-154 DOI:10.1016/j.eng.2025.07.033

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Du Y, Wang T, Xin B, Wang L, Chen Y, Xing L.A data-driven parallel scheduling approach for multiple agile Earth observation satellites.IEEE Trans Evol Comput 2020; 24(4):679-693.

[2]	Li F, Yu H, Ding R, Wang N, Wang Y, Zhou Z.Development strategy of space internet constellation system in China. Strateg Stud Chin Acad Eng 2021;23(4):137–44. Chinese.

[3]	Sun Y, Peng M, Zhang S, Lin G, Zhang P.Integrated satellite–terrestrial networks: architectures, key techniques, and experimental progress.IEEE Netw 2022; 36(6):191-198.

[4]	Ding F, Bao C, Zhou D, Sheng M, Shi Y, Li J.Toward autonomous resource management architecture for 6G satellite–terrestrial integrated networks.IEEE Netw 2024; 38(2):113-121.

[5]	Kuang L, Sun J, Zhang J, Cui H, Liu K.Towards space-based computing infrastructure network: development trends, network architecture, challenges analysis, and key technologies.2025. arXiv: 2503.06521.

[6]	Wang Q, Chen S, Yang C, Qi W, Zong J, Xia X, et al.Energy-efficient task split and resource allocation in LEO-satellite-assisted IoT network.IEEE Internet Things J 2024; 11(21):34519-34527.

[7]

Dai X, Chen X, Jiao L, Wang Y, Du S, Min G.Priority-aware task offloading and resource allocation in satellite and HAP assisted edge–cloud collaborative networks.In: Proceedings of the 15th International Conference on Communication Software and Networks; 2023 Jul 21–23; Shenyang, China. New York City: IEE E; 2023. p. 166–71.

[8]	Hao Y, Song Z, Zheng Z, Zhang Q, Miao Z.Joint communication, computing, and caching resource allocation in LEO satellite MEC networks.IEEE Access 2023; 11:6708-6716.

[9]	Liu J, Zhao X, Qin P, Geng S, Meng S.Joint dynamic task offloading and resource scheduling for WPT enabled space–air–ground power internet of things.IEEE Trans Netw Sci Eng 2022; 9(2):660-677.

[10]	Qin P, Zhao H, Fu Y, Geng S, Chen Z, Zhou H.Energy-efficient resource Al location for space–air–ground integrated industrial power internet of things network.IEEE Trans Industr Inform 2024; 20(4):5274-5284.

[11]	Chen Z, Zhang J, Zheng X, Min G, Li J, Rong C.Profit-aware cooperative offloading in UAV-enabled MEC systems using lightweight deep reinforcement learning.IEEE Internet Things J 2024; 11(12):21325-21336.

[12]	Chen Z, Huang S, Min G, Ning Z, Li J, Zhang Y.Mobility-aware seamless service migration and resource allocation in multi-edge IoV systems.IEEE Trans Mob Comput 2025; 24:6315-6332.

[13]	Chen Z, Jiang Q, Chen L, Chen X, Li J, Min G.MC-2PF: a multi-edge cooperative universal framework for load prediction with personalized federated deep learning.IEEE Trans Mob Comput 2025; 24(6):5138-5154.

[14]	Chen Z, Liang J, Yu Z, Cheng H, Min G, Li J.Resilient collaborative caching for multi-edge systems with robust federated deep learning.IEEE Trans Net 2025; 33(2):654-669.

[15]	Jin Y, Yao H, Mai T.Double auction game-based computing resource allocation in LEO satellite system.In: Proceedings of the 2020 International Wireless Communications and Mobile Computing; 2020 Jun 15–19; Limassol, Cyprus. New York City: IEE E; 2020. p. 274–9.

[16]

Gao Y, Liu J, Geng S, Zhao X, Chen Z, Zhou H.Edge computing task offloading based on game theory for space–air–ground integrated network.In: Proceedings of the 6th International Conference on Communications, Information System and Computer Engineering; 2024 May 10–12; Guangzhou, China. New York City: IEE E; 2024. p. 627–31.

[17]	Fan H, Sun C, Long J, Li L, Huo Y, Wang S.Graph-driven resource allocation strategies in satellite IoT: a cooperative game theoretic approach.IEEE Internet Things J 2025; 12(4):3463-3481.

[18]	Jia Z, Sheng M, Li J, Zhou D, Han Z.Joint HAP access and LEO satellite backhaul in 6G: matching game-based approaches.IEEE J Sel Areas Commun 2021; 39(4):1147-1159.

[19]	Qin P, Wang M, Cai Z, Ding R, Zhao X, Yang F, et al.Optimal resource allocation for AGIN 6G: a learning-based three-sided matching approach.IEEE Trans Netw Sci Eng 2024; 11(2):1553-1565.

[20]	Hu Z, Han C, Gerstacker W, Akyildiz IF.Tera-SpaceCom: GNN-based deep reinforcement learning for joint resource allocation and task offloading in terahertz band space networks.2024. arXiv: 2409.07911.

[21]	Meng X, Wu L, Yu S.Research on resource allocation method of space information networks based on deep reinforcement learning.Remote Sens 2019; 11(4):448.

[22]	Liu J, Wang Y, Dai F, Wang C.Resource allocation strategy of space cloud network based on resource clustering.Int J Commun Syst 2024; 37(14):e5862.

[23]	Wang Y, Liu J, Yin Y, Tong Y, Liu J.Space information network resource scheduling for cloud computing: a deep reinforcement learning approach. Wirel Commun Mob Comput 2022 Jan:1927937.

[24]	Chen Z, Hu J, Min G, Zomaya AY, El-Ghazawi T.Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning.IEEE Trans Parallel Distrib Syst 2020; 31(4):923-934.

[25]	Chen Z, Xiong B, Chen X, Min G, Li J.Joint computation offloading and resource allocation in multi-edge smart communities with personalized federated deep reinforcement learning.IEEE Trans Mobile Comput 2024; 23(12):11604-11619.

[26]	Chen Z, Hu J, Min G, Luo C, El-Ghazawi T.Adaptive and efficient resource allocation in cloud datacenters using actor–critic deep reinforcement learning.IEEE Trans Parallel Distrib Syst 2022; 33(8):1911-1923.

[27]	Chen Z, Huang Z, Zhang J, Cheng H, Li J.Resource allocation and collaborative offloading in multi-UAV-assisted IoV with federated deep reinforcement learning.IEEE Internet Things J 2025; 12(5):4629-4640.

[28]	Chen Z, Zhang J, Min G, Ning Z, Li J.Traffic-aware lightweight hierarchical offloading toward adaptive slicing-enabled SAGIN.IEEE J Sel Areas Commun 2024; 42(12):3536-3550.

[29]	Li L, Chu W, Langford JJ, Schapire RE. A contextual-bandit approach to personalized news article recommendation, Association for Computing Machinery (ACM), CA, USA. New York City (2010), pp. 661-670

[30]	Yang M, Li Q, Qin Z, Ye J.Hierarchical adaptive contextual bandits for resource constraint based recommendation.In: Proceedings of the Web Conference 2020; 2020 Apr 20–24; Taipei, China. New York City: Association for Computing Machinery (ACM); 2020. p. 292–302.

[31]	Baheri A.Multilevel constrained bandits: a hierarchical upper confidence bound approach with safety guarantees.Mathematics 2025; 13:149.

[32]	Hanna OA, Yang LF, Fragouli C.Learning in distributed contextual linear bandits without sharing the context.2022. arXiv: 2206.04180.

[33]	Li Y, Mu Z, Qi S.A contextual combinatorial bandit approach to negotiation.2024. arXiv: 2407.00567.

[34]	Feng Y, Shen H, Shan Z, Yang Q, Shi X.Semantic communication for edge intelligence enabled autonomous driving system.2024. arXiv: 2406.10606.

[35]	Liu S, Wen D, Li D, Chen Q, Zhu G, Shi Y.Energy-efficient optimal mode selection for edge AI inference via integrated sensing–communication–computation.IEEE Trans Mobile Comput 2024; 23(12):14248-14262.

[36]	Yang Z, Chen M, Zhang Z, Huang C.Energy efficient semantic communication over wireless networks with rate splitting.IEEE J Sel Areas Commun 2023; 41(5):1484-1495.

[37]	Xu W, Yang Z, Ng DWK, Levorato M, Eldar YC, Debbah M.Edge learning for B5G networks with distributed signal processing: semantic communication, edge computing, and wireless sensing.IEEE J Sel Top Signal Process 2023; 17(1):9-39.

[38]	Wang Y, Yang C, Yu Y, Li Y, Li D, Zhao X, et al.A brief survey and implementation on network-level intent decomposition for telecommunication networks.IEEE Commun Mag 2025; 63(7):178-186.

[39]	Cui L, Jia W.Cyclic stable matching for three-sided networking services.Comput Netw 2013; 57:351-363.

[40]	Roth AE.The evolution of the labor market for medical interns and residents: a case study in game theory.J Polit Econ 1984; 92(6):991-1016.

[41]	Leng T, Li X, Hu D, Cui G, Wang W.Collaborative computing and resource allocation for LEO satellite-assisted Internet of things. Wirel Commun Mob Comput 2021 Sep:4212548.

[42]	Lyu T, Xu H, Liu F, Li M, Li L, Han Z.Two layer stackelberg game-based resource allocation in cloud–network convergence service computing.IEEE Trans Cogn Commun Netw 2024; 10(6):2412-2426.

[43]	Ding Y, Li K, Liu C, Li K.A potential game theoretic approach to computation offloading strategy optimization in end–edge–cloud computing.IEEE Trans Parallel Distrib Syst 2022; 33(6):1503-1519.