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相关概念视频

Distributed Loads01:19

Distributed Loads

529
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
529
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

642
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...
642
Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

630
Understanding the relationship between the distributed load and shear force in structural analysis is crucial for analyzing beams subjected to various loading conditions. Consider the case of a beam experiencing a distributed load, two concentrated loads, and a couple moment.
630
Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

1.8K
In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
1.8K
Beams with Symmetric Loadings01:15

Beams with Symmetric Loadings

186
The moment-area method is an analytical tool used in structural engineering to determine the slope and deflection of beams under various loads. Consider a cantilever with a concentrated load and moment at the free end. The first step is constructing a free-body diagram to calculate the reactions at the fixed end. Next, the bending moment diagram is plotted to visualize how the bending moment varies along the beam's length, focusing on points where the bending moment equals zero.
The M/EI...
186
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.5K
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).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.5K

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相关实验视频

Updated: Jun 24, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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Published on: September 8, 2023

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SJFO:Sail Jelly Fish优化启用了基于DRNN预测的VM迁移,用于云计算中的负载平衡.

Rajesh Rathinam1, Premkumar Sivakumar2, Sivakumar Sigamani3

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.

Network (Bristol, England)
|June 3, 2024
PubMed
概括

本研究介绍了Sail Jelly Fish优化 (SJFO) 算法,用于在云计算中高效的虚拟机 (VM) 迁移. SJFO优化负载平衡,提高云资源管理和性能.

关键词:
云计算是一种云计算.深度循环神经网络 (DRNN) 是一种深度循环神经网络.鱼优化器 (SFO) 是一个优化器.虚拟机 (VM) 是一个虚拟机.

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科学领域:

  • 云计算 云计算 云计算 云计算
  • 人工智能的人工智能
  • 优化算法 优化算法

背景情况:

  • 云环境中的动态工作负载需要有效的负载平衡策略.
  • 虚拟机 (VM) 迁移是管理物理机器 (PM) 之间的资源分配的一个关键技术.
  • 现有的负载平衡方法需要增强,以在复杂的云架构中实现最佳性能.

研究的目的:

  • 提出一种新的优化算法,即Sail Jelly Fish Optimization (SJFO),用于基于VM迁移的负载均衡.
  • 提高云数据中心负载分配和资源利用的效率.
  • 评估拟议的SJFO算法在管理动态工作负载方面的性能.

主要方法:

  • 通过结合Sail Fish Optimizer (SFO) 和水母搜索 (JS) 算法开发了SJFO.
  • 集成深度循环神经网络 (DRNN) 用于准确的负载预测.
  • 由预测负载超过定义值引发的实施VM迁移.

主要成果:

  • 在负载均衡方面,SJFO算法表现出了卓越的性能.
  • 实现了0.598.8的优越容量指标.
  • 显示了0.089的劣势负载指标和0.257的劣势资源利用指标,表明效率有所提高.

结论:

  • 拟议的SJFO-VM迁移策略有效地平衡了云环境中的动态工作负载.
  • 与云资源管理的现有方法相比,SJFO提供了显著的改进.
  • 集成DRNN用于负载预测进一步提高了拟议解决方案的有效性.