在雾云环境中为IOT采用动态任务调度的新方法
A Mindil1, Ahmed Y Hamed2,3, Moatamad R Hassan4
1Department of Physical Sciences, College of Science, University of Jeddah, Jeddah, 23890, Saudi Arabia. amindil@uj.edu.sa.
Scientific reports
|January 22, 2026
概括
一个新的量子启发的偏向动态调度器 (QBDS) 通过平衡延迟,能量和成本来优化物联网 (IoT) 调度. 这一框架在复杂的物联网-雾-云环境中提高了资源利用率和性能.
科学领域:
- 计算机科学 计算机科学
- 分布式系统 分布式系统
- 人工智能的人工智能
背景情况:
- 实时物联网 (IoT) 应用程序的扩散挑战了集中式云计算.
- 跨物联网,雾和云层的分布式计算引入了复杂的调度需求.
- 现有的调度人员在与异质的资源,动态的任务到达以及具有竞争力的目标 (如延迟,能量和成本) 斗争.
研究的目的:
- 为物联网-雾-云连续性引入一种新的调度框架,即以量子为灵感的偏差动态调度器 (QBDS).
- 优化一个可配置的复合目标函数 (COF),它集成了制造范围,能源消耗,成本,负载平衡,资源利用和时间指标.
- 在动态和资源受限的边缘计算环境中解决当前调度方法的局限性.
主要方法:
- 开发了一个优先级意识的任务排名机制,以适应方式权衡任务特征 (截止日期宽松,执行长度,内存,数据大小).
- 实施了一种形,量子启发的偏差技术来扰乱任务和节点指标,帮助探索和逃避局部最佳.
- 使用了惩罚意识的多目标成本评估器来做出明智的任务对节点分配决策.
主要成果:
- QBDS持续改善关键绩效指标,包括制造商,能源消耗,成本和资源利用.
- 在沉重的计算负载和多样化的网络拓结构下展示了强大的性能扩展.
- 废弃性研究证实了QBDS框架内的单个成分的有效性.
结论:
- 在层次计算环境中,QBDS为延迟敏感的物联网应用提供了卓越的调度解决方案.
- 量子启发的方法有效地平衡了多个,往往相互冲突的优化目标.
- QBDS提供了一种可扩展和高效的方法,用于在不断变化的物联网-雾-云环境中管理资源.
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