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

被引:0
作者
Liu, Xing [1 ]
Lv, Jianhui [2 ,3 ,4 ]
Kim, Byung-Gyu [5 ]
Li, Keqin [6 ]
Jin, Hongkai [7 ]
Gao, Wei [8 ]
Bai, Jiayuan [9 ]
机构
[1] Jinzhou Med Univ, Affiliated Hosp 1, Dept Oncol, Jinzhou 121012, Peoples R China
[2] Jinzhou Med Univ, Affiliated Hosp 1, Dept Imaging, Jinzhou 121012, Peoples R China
[3] Peng Cheng Lab, Dept Networks, Shenzhen 518057, Peoples R China
[4] Tsinghua Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China
[5] Sookmyung Womens Univ, Dept Artificial Intelligence Engn, Seoul 04310, South Korea
[6] SUNY Coll New Paltz, Dept Comp Sci, New Paltz, NY 12561 USA
[7] Jinzhou Med Univ, Sch Life Sci, Jinzhou 121001, Peoples R China
[8] Jinzhou Med Univ, Sch Basic Med, Jinzhou 121001, Peoples R China
[9] Jinzhou Med Univ, Sch Clin Med 1, Jinzhou 121001, Peoples R China
来源
TSINGHUA SCIENCE AND TECHNOLOGY | 2025年 / 30卷 / 02期
基金
中国国家自然科学基金;
关键词
Costs; Scheduling algorithms; Computational modeling; Estimation; Robustness; Scheduling; Data models; Electronic healthcare; Resource management; Edge computing; digital healthcare; task scheduling; resource management; edge intelligence;
D O I
10.26599/TST.2024.9010140
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:926 / 945
页数:20
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