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相关概念视频

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

636
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...
636
Machines: Problem Solving II01:30

Machines: Problem Solving II

303
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Machines: Problem Solving I01:22

Machines: Problem Solving I

310
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
310
Parallel Processing01:20

Parallel Processing

147
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...
147
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

105
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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深度学习和优化实现了多目标,用于云计算中的任务调度.

Dinesh Komarasamy1, Siva Malar Ramaganthan2, Dharani Molapalayam Kandaswamy3

  • 1Department of Computer Science and Engineering, Kongu Engineering College, Erode, India.

Network (Bristol, England)
|August 20, 2024
PubMed
概括

本研究介绍了一种新的云计算任务调度模型,使用混合分数火甲虫优化 (FFBO) 和深度学习 (DL) 来提高效率. FFBO-DL模型优化了基于可靠性,成本,能量和时长的任务分配,实现了卓越的性能.

关键词:
任务安排 任务安排云计算 (CC) 是一种云计算.深度学习 (DL) 是指深度学习.甲甲虫优化 (DBO) 的方法

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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:

  • 在云计算中,任务调度对于有效的资源配置至关重要.
  • 现有的方法往往难以平衡成本,能源和性能等多个目标.
  • 深度学习和先进的优化技术为改进调度提供了潜力.

研究的目的:

  • 提出一种新的混合模型,用于云计算中的多目标任务调度.
  • 整合深度学习用于能源预测和资源配置优化算法.
  • 通过考虑任务和虚拟机参数来提高整体系统效率.

主要方法:

  • 一种混合分数火甲虫优化 (FFBO) 算法,结合了甲虫优化 (DBO),火甲虫搜索算法 (FSA) 和分数计算 (FC).
  • 深度残留网络 (DRN) 用于预测能源消耗.
  • 深度Feedforward神经网络融合了长期短期记忆 (DFNN-LSTM) 用于任务调度.
  • 考虑任务参数 (EFT,EST,任务长度,优先级,运行时间) 和VM参数 (CPU,内存,带宽,容量).

主要成果:

  • 拟议的DFNN-LSTM+FFBO模型在关键指标上表现出卓越的表现.
  • 实现了0.188的产量,0.950J的能源消耗,0.238.8的资源利用率.
  • 与现有的任务安排方法相比,显示出了显著的改进.

结论:

  • 集成的DFNN-LSTM+FFBO模型有效地解决了云计算中的多目标任务调度.
  • 混合方法显示出优化云资源管理的前景.
  • 进一步的研究可以探索可扩展性和现实世界的部署.