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相关概念视频

Singularity Functions for Shear01:26

Singularity Functions for Shear

148
In structural analysis, singularity functions are crucial in simplifying the representation of shear forces in beams under discontinuous loading. These functions describe discontinuous  variations in shear force across a beam with varying loads by using a single mathematical expression, regardless of the complexity of the loading conditions. The singularity functions are derived from creating a free-body diagram of the beam and then making conceptual cuts at specific points to examine the...
148
Controller Configurations01:22

Controller Configurations

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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
115
Deflection of a Beam01:19

Deflection of a Beam

284
Accurately determining beam deflection and slope under various loading conditions in structural engineering is crucial for ensuring safety and structural integrity. Singularity functions offer a streamlined approach to analyzing beams, especially when multiple loading functions complicate the bending moment equation.
Singularity functions, described in an earlier lesson, are powerful mathematical tools that represent discontinuities within a function commonly encountered in structural loading...
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Elasticity01:12

Elasticity

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Elasticity is the ability of an object to withstand the effects of distortion and to return to its original size and shape once the forces causing deformation are removed. When an elastic material deforms under the action of an external force, it experiences internal resistance to the deformation. However, if no external force is applied, it returns to its original state.
The elasticity of an object can be described by a stress-strain curve, which represents the relationship between stress...
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Lift01:23

Lift

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Lift is a fundamental aerodynamic force that acts perpendicular to the direction of airflow. It plays a central role in achieving and sustaining flight and in stabilizing various vehicles. Lift primarily originates from pressure differences created across surfaces, such as an airfoil. A lower pressure region forms above the wing, while a higher pressure region forms below it, generating an upward force. This differential results from the shape and orientation of the airfoil, enabling the wing...
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Midrange01:07

Midrange

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A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
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A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area Networks.

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火脸:利用内部功能功能在无服务器边缘平台上的功能配置.

Ming Li1,2,3, Jianshan Zhang4, Jingfeng Lin1,2,3

  • 1College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
概括

通过预测函数执行时间和使用APSO-GA来选择具有成本效益的资源配置,FireFace优化了无服务器边缘计算,降低了高达44.8%的开支. 这种适应性方法尽量减少财务开销,同时实现服务水平目标 (SLO).

关键词:
这是一个慢慢的过程.配置优化 配置优化作为一种服务的功能.无服务器计算是无服务器计算.

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科学领域:

  • 计算机科学 计算机科学
  • 分布式计算 (Distributed Computing) 是一种分布式计算.
  • 云计算 云计算 云计算

背景情况:

  • 无服务器计算是一种流行的云应用程序部署模型,抽象基础设施管理.
  • 现有的无服务器资源配置方法依赖于历史数据或插值,这对于边缘平台来说是低效的.
  • 无服务器边缘平台面临的挑战是资源异质性和更高的开销,增加了开发人员的成本.

研究的目的:

  • 提出一种自适应和高效的方法,FireFace,用于优化无服务器边缘计算资源配置.
  • 尽量减少开发人员的财务开销,同时确保服务水平目标 (SLO) 得到满足.
  • 解决无服务器边缘平台中资源异质性和动态环境的挑战.

主要方法:

  • 开发了一个预测模块,根据内部功能特征和配置方案预测功能执行时间.
  • 实现了一个使用自适应粒子集群优化和遗传算法操作员 (APSO-GA) 算法的决策模块.
  • 决策模块分析环境信息,为CPU,内存和边缘平台选择最佳配置.

主要成果:

  • 预测模型在所有指标上取得了最佳结果,实际无服务器应用的预测错误率为4.25%9.51%.
  • 与经典算法相比,FireFace通过找到最佳的资源配置,实现了7.2%44.8%的平均成本节约.
  • 该方法表现出快速的适应性,有效地调整动态环境中的资源分配.

结论:

  • 在满足SLO的同时,FireFace有效地将无服务器边缘计算的财务开销降到最低.
  • 拟议的方法比现有解决方案提供了显著的成本节约和提高效率.
  • 对于无服务器边缘资源管理的复杂性,FireFace提供了一个强大的,可适应的解决方案.