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

IF 6.6 1区 计算机科学 Q1 Multidisciplinary
Xing Liu;Jianhui Lv;Byung-Gyu Kim;Keqin Li;Hongkai Jin;Wei Gao;Jiayuan Bai
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引用次数: 0

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.
边缘智能系统中的协同数字医疗任务调度和资源管理
数字医疗保健应用程序的快速增长导致对边缘计算环境中高效可靠的任务调度和资源管理的需求不断增加。然而,有限的边缘服务器资源和处理延迟敏感型医疗保健任务的需求构成了重大挑战。现有的解决方案通常需要帮助来平衡系统成本和服务质量之间的权衡,特别是在资源受限的场景中。为了应对这些挑战,我们为边缘智能系统中的数字医疗应用程序提出了一种新的协作任务调度和资源管理框架。我们的方法利用了一种两步优化策略,该策略结合了用于任务调度的多臂组合选择问题(MCSP)和用于计算卸载的具有替代奖励估计的顺序马尔可夫决策过程(SMDP)。基于mcsp的调度算法有效地探索组合任务调度空间,以最小化医疗保健任务的完成时间和成本。基于smdp的卸载策略结合了可选的奖励估计,以提高对系统环境动态变化的鲁棒性。使用真实医疗保健数据的大量模拟表明,与最先进的基线相比,我们提出的框架具有优越的性能,在成本、任务成功率和公平性方面取得了显著改善。所提出的方法可在资源受限的边缘计算环境中实现可靠和高效的数字医疗保健服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Tsinghua Science and Technology
Tsinghua Science and Technology COMPUTER SCIENCE, INFORMATION SYSTEMSCOMPU-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
10.20
自引率
10.60%
发文量
2340
期刊介绍: Tsinghua Science and Technology (Tsinghua Sci Technol) started publication in 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, electronic engineering, and other IT fields. Contributions all over the world are welcome.
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