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相关概念视频

Distributed Loads: Problem Solving01:21

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
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在雾计算中使用模拟回火的优先级识别多目标任务安排.

S Sudheer Mangalampalli1, Pillareddy Vamsheedhar Reddy2, Ganesh Reddy Karri3

  • 1Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 560064, India.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
概括
此摘要是机器生成的。

一个新的模拟回火 (SA) 框架优化了多媒体物联网 (IoMT) 的任务安排. 这种方法通过将延迟,能源使用和成本降至最低,同时优先考虑关键任务,从而增强了雾计算.

关键词:
雾计算 雾计算 雾计算多目标优化多目标优化模拟回火 (SA) 模拟回火任务安排任务安排.

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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:

  • 物联网 (IoT) 设备和多媒体物联网 (IoMT) 应用程序的快速增长使传统的云计算基础设施因延迟和资源分配问题而受到压力.
  • 雾计算提供了一个分散的解决方案,通过将计算更接近数据源来缓解这些挑战.

研究的目的:

  • 解决多目标任务安排在异质,资源有限的雾环境中的NP难题.
  • 开发一种基于模拟回火 (SA) 的新型框架,以优化雾计算中的任务调度.

主要方法:

  • 提出了一个模拟回火 (SA) 算法,用于在雾计算环境中的任务调度.
  • 集成了一个优先级感知惩罚功能,以提高对高优先级任务的响应能力.
  • 针对多个目标进行了优化:makepan,能源消耗和执行成本.

主要成果:

  • 与ACO,PSO,I-FASC和M2MPA相比,基于SA的调度器在各种指标上表现出卓越的表现.
  • 实现了使,能源消耗和执行成本大幅降低.
  • 在不同任务量中展示了高可靠性和可扩展性.

结论:

  • 拟议的基于SA的框架是一个可扩展和有效的解决方案,用于智能任务调度,在雾支持的物联网基础设施.
  • 该方法成功地平衡了多目标优化与优先级敏感任务执行.