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相关概念视频

Distributed Loads: Problem Solving01:21

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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用GA-PSO算法优化雾计算中的多目标任务调度,用于大数据应用程序.

Muhammad Saad1,2, Rabia Noor Enam1, Rehan Qureshi3

  • 1Computer Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan.

Frontiers in big data
|March 7, 2024
PubMed
概括
此摘要是机器生成的。

一个新的混合基因算法 (GA) -粒子群集优化 (PSO) 有效地安排在雾计算环境中的任务. 这种方法显著减少了大数据处理的执行,响应和完成时间.

关键词:
云计算是云计算中的一个.雾计算 雾计算 雾计算雾计算 (FC) 的使用遗传算法是一种遗传算法.混合型GA-PSO是什么混合算法是混合算法.粒子群集优化 粒子群集优化任务安排任务安排.

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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:

  • 传统的云计算面临着挑战,大数据的数量和速度不断增加,特别是在实时处理和低延迟方面.
  • 雾计算通过利用边缘设备提供分布式解决方案,但由于多目标优化需求,高效的任务调度仍然是复杂的.
  • 关键的挑战包括在雾环境中平衡执行时间,响应时间和资源利用.

研究的目的:

  • 提出一种新的混合算法,用于优化雾计算中的多目标任务调度.
  • 通过结合遗传算法 (GA) 和粒子群优化 (PSO) 的优势来提高任务调度的性能.
  • 在复杂的雾计算场景中解决传统单个算法方法的局限性.

主要方法:

  • 开发一种混合基因算法 (GA) -粒子优化 (PSO) 算法.
  • 在模拟雾计算环境中实现用于多目标任务调度的混合算法.
  • 混合算法与独立的GA,PSO和混合PWOA算法的比较分析.

主要成果:

  • 混合GA-PSO算法显示了执行时间的显著改善 (高达85.68%与GA相比,84%与GA相比). 混合PWOA,51.03%与PSO相比).
  • 观察到响应时间大幅减少 (高达67.28%与GA相比,54.24%与GA相比). 混合PWOA,75.40%与PSO相比).
  • 在完成时间方面取得了显著的改进 (高达68.69%与GA相比,98.91%与GA相比). 混合PWOA,75.90%与PSO) 在各种任务输入和雾节点.

结论:

  • 混合GA-PSO算法有效地优化了雾计算中的多目标任务调度.
  • 与传统的单个算法方法相比,这种方法提供了更高的性能,解决了大数据处理需求.
  • 这些发现表明,在分布式边缘计算环境中,对高效的资源管理有希望的方向.