AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (1.7 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Cooperative Digital Healthcare Task Scheduling and Resource Management in Edge Intelligence Systems

Department of Oncology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121012, China
Department of Imaging, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121012, China, and with Department of Networks, Peng Cheng Laboratory, Shenzhen 518057, China, and also with Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China
Department of Artificial Intelligence Engineering, Sookmyung Women’s University, Seoul 04310, Republic of Korea
Department of Computer Science, State University of New York, New Paltz, NY 12561, USA
School of Life Sciences, Jinzhou Medical University, Jinzhou 121001, China
School of Basic Medicine, Jinzhou Medical University, Jinzhou 121001, China
School of First Clinical Medicine, Jinzhou Medical University, Jinzhou 121001, China
Show Author Information

Abstract

The rapid growth of digital healthcare applications has led to an increasing demand for efficient and reliable task scheduling and resource management in edge computing environments. However, the limited resources of edge servers and the need to process delay-sensitive healthcare tasks pose significant challenges. Existing solutions often need help to balance the trade-off between system cost and quality of service, particularly in resource-constrained scenarios. To address these challenges, we propose a novel cooperative task scheduling and resource management framework for digital healthcare applications in edge intelligence systems. Our approach leverages a two-step optimization strategy that combines the Multi-armed Combinatorial Selection Problem (MCSP) for task scheduling and the Sequential Markov Decision Process (SMDP) with alternative reward estimation for computation offloading. The MCSP-based scheduling algorithm efficiently explores the combinatorial task scheduling space to minimize healthcare task completion time and costs. The SMDP-based offloading strategy incorporates alternative reward estimation to improve robustness against dynamic variations in the system environment. Extensive simulations using real-world healthcare data demonstrate the superior performance of our proposed framework compared to state-of-the-art baselines, achieving significant improvements in cost, task success rate, and fairness. The proposed approach enables reliable and efficient digital healthcare services in resource-constrained edge computing environments.

Keywords

digital healthcaretask schedulingresource managementedge intelligence

References

[1]

B. Picano, E. Vicario, and R. Fantacci, An efficient flows dispatching scheme for tardiness minimization of data-intensive applications in heterogeneous systems, IEEE Trans. Netw. Sci. Eng., vol. 10, no. 6, pp. 3232–3241, 2023.
Crossref Google Scholar
[2]

J. Liang, B. Ma, Z. Feng, and J. Huang, Reliability-aware task processing and offloading for data-intensive applications in edge computing, IEEE Trans. Netw. Serv. Manag., vol. 20, no. 4, pp. 4668–4680, 2023.
Crossref Google Scholar
[3]

L. Kong, J. Tan, J. Huang, G. Chen, S. Wang, X. Jin, P. Zeng, M. Khan, and S. K. Das, Edge-computing-driven Internet of Things: A survey, ACM Comput. Surv., vol. 55, no. 8, pp. 1–41, 2023.
Crossref Google Scholar
[4]

R. Luo, H. Jin, Q. He, S. Wu, and X. Xia, Cost-effective edge server network design in mobile edge computing environment, IEEE Trans. Sustain. Comput., vol. 7, no. 4, pp. 839–850, 2022.
Crossref Google Scholar
[5]

Y. Chen, F. Zhao, Y. Lu, and X. Chen, Dynamic task offloading for mobile edge computing with hybrid energy supply, Tsinghua Science and Technology, vol. 28, no. 3, pp. 421–432, 2023.
Crossref Google Scholar
[6]

X. Lv, S. Rani, S. Manimurugan, A. Slowik, and Y. Feng, Quantum-inspired sensitive data measurement and secure transmission in 5G-enabled healthcare systems, Tsinghua Science and Technology, vol. 30, no. 1, pp. 456–478, 2025.
Crossref Google Scholar
[7]

H. Baghban, A. Rezapour, C.-H. Hsu, S. Nuannimnoi, and C.-Y. Huang, Edge-AI: IoT request service provisioning in federated edge computing using actor-critic reinforcement learning, IEEE Trans. Eng. Manag., vol. 71, pp. 12519–12528, 2024.
Crossref Google Scholar
[8]

H. Wang, H. Xu, H. Huang, M. Chen, and S. Chen, Robust task offloading in dynamic edge computing, IEEE Trans. Mob. Comput., vol. 22, no. 1, pp. 500–514, 2023.
Crossref Google Scholar
[9]

H. Ko, J. Kim, D. Ryoo, I. Cha, and S. Pack, A belief-based task offloading algorithm in vehicular edge computing, IEEE Trans. Intell. Transp. Syst., vol. 24, no. 5, pp. 5467–5476, 2023.
Crossref Google Scholar
[10]

J. Zhang, J. Chen, X. Bao, C. Liu, P. Yuan, X. Zhang, and S. Wang, Dependent task offloading mechanism for cloud-edge-device collaboration, J. Netw. Comput. Appl., vol. 216, p. 103656, 2023.
Crossref Google Scholar
[11]

H. Xu, J. Zhou, W. Wei, and B. Cheng, Multiuser computation offloading for long-term sequential tasks in mobile edge computing environments, Tsinghua Science and Technology, vol. 28, no. 1, pp. 93–104, 2023.
Crossref Google Scholar
[12]

X. Ma, A. Zhou, S. Zhang, Q. Li, A. X. Liu, and S. Wang, Dynamic task scheduling in cloud-assisted mobile edge computing, IEEE Trans. Mob. Comput., vol. 22, no. 4, pp. 2116–2130, 2023.
Crossref Google Scholar
[13]

Z. Tang, W. Jia, X. Zhou, W. Yang, and Y. You, Representation and reinforcement learning for task scheduling in edge computing, IEEE Trans. Big Data, vol. 8, no. 3, pp. 795–808, 2022.
Crossref Google Scholar
[14]

J. Mendez, K. Bierzynski, M. P. Cuéllar, and D. P. Morales, Edge intelligence: Concepts, architectures, applications, and future directions, ACM Trans. Embed. Comput. Syst., vol. 21, no. 5, pp. 1–41, 2022.
Crossref Google Scholar
[15]
T. Zhang, G. Li, S. Wang, G. Zhu, G. Chen, and R. Wang, ISAC-accelerated edge intelligence: Framework, optimization, and analysis, IEEE Trans. Green Commun. Netw., vol. 7, no. 1, pp. 455–468, 2023.
Crossref
[16]

S. Zhu, K. Ota, and M. Dong, Energy-efficient artificial intelligence of things with intelligent edge, IEEE Internet Things J., vol. 9, no. 10, pp. 7525–7532, 2022.
Crossref Google Scholar
[17]

J.-H. Syu, J. C.-W. Lin, G. Srivastava, and K. Yu, A comprehensive survey on artificial intelligence empowered edge computing on consumer electronics, IEEE Trans. Consum. Electron., vol. 69, no. 4, pp. 1023–1034, 2023.
Crossref Google Scholar
[18]

W. Cao, W. Shen, Z. Zhang, and J. Qin, Privacy-preserving healthcare monitoring for IoT devices under edge computing, Comput. Secur., vol. 134, p. 103464, 2023.
Crossref Google Scholar
[19]

M. Izhar, S. A. Ali Naqvi, A. Ahmed, S. Abdullah, N. Alturki, and L. Jamel, Enhancing healthcare efficacy through IoT-edge fusion: A novel approach for smart health monitoring and diagnosis, IEEE Access, vol. 11, pp. 136456–136467, 2023.
Crossref Google Scholar
[20]

K. Peng, P. Liu, M. Bilal, X. Xu, and E. Prezioso, Mobility and privacy-aware offloading of AR applications for healthcare cyber-physical systems in edge computing, IEEE Trans. Netw. Sci. Eng., vol. 10, no. 5, pp. 2662–2673, 2023.
Crossref Google Scholar
[21]

Y. Gao, S. Ni, D. Wu, and L. Zhou, Edge-based cross-modal communications for remote healthcare, IEEE J. Sel. Areas Commun., vol. 40, no. 11, pp. 3139–3151, 2022.
Crossref Google Scholar
[22]

N. TaheriNejad, P. Perego, and A. M. Rahmani, Mobile health technology: From daily care and pandemics to their energy consumption and environmental impact, Mob. Netw. Appl., vol. 27, no. 2, pp. 652–656, 2022.
Crossref Google Scholar
[23]

J. Wang, M. Dong, B. Liang, G. Boudreau, and H. Abou-Zeid, Delay-tolerant OCO with long-term constraints: Algorithm and its application to network resource allocation, IEEE/ACM Trans. Netw., vol. 31, no. 1, pp. 147–163, 2023.
Crossref Google Scholar
[24]

X. Shao, G. Hasegawa, M. Dong, Z. Liu, H. Masui, and Y. Ji, An online orchestration mechanism for general-purpose edge computing, IEEE Trans. Serv. Comput., vol. 16, no. 2, pp. 927–940, 2023.
Crossref Google Scholar
[25]

J. Gao, R. Chang, Z. Yang, Q. Huang, Y. Zhao, and Y. Wu, A task offloading algorithm for cloud-edge collaborative system based on Lyapunov optimization, Clust. Comput., vol. 26, no. 1, pp. 337–348, 2023.
Crossref Google Scholar
[26]

T. Gen, Y. Ito, T. Kimura, and K. Hirata, Design of multi-armed bandit-based routing for in-network caching, IEEE Access, vol. 11, pp. 82584–82600, 2023.
Crossref Google Scholar
[27]

Y. Wen, Q. Su, M. Shen, and N. Xiao, Improving the exploration efficiency of DQNs via the confidence bound methods, Appl. Intell., vol. 52, no. 13, pp. 15447–15461, 2022.
Crossref Google Scholar
[28]

M. Park, J. Shin, and I. Yang, Anderson acceleration for partially observable Markov decision processes: A maximum entropy approach, Automatica, vol. 163, p. 111557, 2024.
Crossref Google Scholar
[29]

J. P. Araújo, M. A. T. Figueiredo, and M. Ayala Botto, Control with adaptive Q-learning: A comparison for two classical control problems, Eng. Appl. Artif. Intell., vol. 112, p. 104797, 2022.
Crossref Google Scholar
[30]

Y. Qu, H. Dai, L. Wang, W. Wang, F. Wu, H. Tan, S. Tang, and C. Dong, CoTask: Correlation-aware task offloading in edge computing, World Wide Web, vol. 25, no. 5, pp. 2185–2213, 2022.
Crossref Google Scholar
[31]
S. Jiao, H. Wang, and J. Luo, SRA-E-ABCO: Terminal task offloading for cloud-edge-end environments, J. Cloud Comput., vol. 13, no. 1, p. 58, 2024.
Crossref
[32]

H. Zhang, L. Chen, J. Cao, X. Zhang, S. Kan, and T. Zhao, Traffic flow forecasting of graph convolutional network based on spatio-temporal attention mechanism, Int. J. Automot. Technol., vol. 24, no. 4, pp. 1013–1023, 2023.
Crossref Google Scholar
[33]
Y. Li, D. Zeng, L. Gu, A. Zhu, Q. Chen, and S. Yu, PASTO: Enabling secure and efficient task offloading in TrustZone-enabled edge clouds, IEEE Trans. Veh. Technol., vol. 72, no. 6, pp. 8234–8238, 2023.
Crossref
[34]
L. Song, G. Sun, H. Yu, and M. Guizani, SD-AETO: Service-deployment-enabled adaptive edge task offloading scheme in MEC, IEEE Internet Things J., vol. 10, no. 21, pp. 19296–19311, 2023.
Crossref
[35]

S. Yao, M. Wang, Q. Qu, Z. Zhang, Y.-F. Zhang, K. Xu, and M. Xu, Blockchain-empowered collaborative task offloading for cloud-edge-device computing, IEEE J. Sel. Areas Commun., vol. 40, no. 12, pp. 3485–3500, 2022.
Crossref Google Scholar
[36]

K. Yu, Q. Yu, Z. Tang, J. Zhao, B. Qian, Y. Xu, H. Zhou, and X. Shen, Fully-decoupled radio access networks: A flexible downlink multi-connectivity and dynamic resource cooperation framework, IEEE Trans. Wirel. Commun., vol. 22, no. 6, pp. 4202–4214, 2023.
Crossref Google Scholar
[37]

Z. Ilhan Taskin, K. Yildirak, and C. H. Aladag, An enhanced random forest approach using CoClust clustering: MIMIC-III and SMS Spam collection application, J. Big Data, vol. 10, no. 1, p. 38, 2023.
Crossref Google Scholar
[38]

Y. Kumar and B. Gupta, Retinal image blood vessel classification using hybrid deep learning in cataract diseased fundus images, Biomed. Signal Process. Contr., vol. 84, p. 104776, 2023.
Crossref Google Scholar
[39]

O. Abuajwa, M. Bin Roslee, Z. Binti Yusoff, L. C. Lee, and W. L. Pang, Throughput fairness trade-offs for downlink non-orthogonal multiple access systems in 5G networks, Heliyon, vol. 8, no. 11, p. e11265, 2022.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 30 Issue 2,
April 2025
Pages 926-945
DOI: 10.26599/TST.2024.9010140
Cite this article:
Liu X, Lv J, Kim B-G, et al. Cooperative Digital Healthcare Task Scheduling and Resource Management in Edge Intelligence Systems. Tsinghua Science and Technology, 2025, 30(2): 926-945. https://doi.org/10.26599/TST.2024.9010140

81

Views

6

Downloads

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics
Received: 04 June 2024
Revised: 21 July 2024
Accepted: 31 July 2024
Published: 09 December 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return