利用增强核 SVM 为电子医疗提供服务感知的分层雾-云资源映射

IF 3.3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Alaa AlZailaa, Hao Ran Chi, A. Radwan, Rui L. Aguiar
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引用次数: 0

摘要

基于雾云的分层任务调度方法因用户数量大、任务多样性高、服务级要求苛刻等特点,在支持电子健康应用方面面临着巨大挑战。针对雾云一体化的挑战,本文提出了一种新的服务/网络感知雾云分层资源映射方案,实现了电子医疗应用中服务级关键任务的资源利用效率最优化和延迟最小化。具体来说,我们开发了一种服务/网络感知任务分类算法。我们采用计算速度快的支持向量机作为骨干,支持实时任务调度,并开发了一种融合卷积、交叉相关和自相关的新内核,以增强特异性和灵敏度。在任务分类的基础上,我们提出了任务优先级分配和资源映射算法,旨在实现关键任务的整体延迟最小化,并提高资源利用效率。仿真结果表明,在不同的网络设置下,所提出的算法能使关键任务/非关键任务的平均执行时间分别达到 0.23/0.50 毫秒,分别比基准方案超出 73.88%/52.01% 。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM
Fog–cloud-based hierarchical task-scheduling methods are embracing significant challenges to support e-Health applications due to the large number of users, high task diversity, and harsher service-level requirements. Addressing the challenges of fog–cloud integration, this paper proposes a new service/network-aware fog–cloud hierarchical resource-mapping scheme, which achieves optimized resource utilization efficiency and minimized latency for service-level critical tasks in e-Health applications. Concretely, we develop a service/network-aware task classification algorithm. We adopt support vector machine as a backbone with fast computational speed to support real-time task scheduling, and we develop a new kernel, fusing convolution, cross-correlation, and auto-correlation, to gain enhanced specificity and sensitivity. Based on task classification, we propose task priority assignment and resource-mapping algorithms, which aim to achieve minimized overall latency for critical tasks and improve resource utilization efficiency. Simulation results showcase that the proposed algorithm is able to achieve average execution times for critical/non-critical tasks of 0.23/0.50 ms in diverse networking setups, which surpass the benchmark scheme by 73.88%/52.01%, respectively.
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来源期刊
Journal of Sensor and Actuator Networks
Journal of Sensor and Actuator Networks Physics and Astronomy-Instrumentation
CiteScore
7.90
自引率
2.90%
发文量
70
审稿时长
11 weeks
期刊介绍: Journal of Sensor and Actuator Networks (ISSN 2224-2708) is an international open access journal on the science and technology of sensor and actuator networks. It publishes regular research papers, reviews (including comprehensive reviews on complete sensor and actuator networks), and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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