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Distributed Loads01:19

Distributed Loads

538
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...
538
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

646
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...
646
Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

641
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.
641
Elastic Curve from the Load Distribution01:16

Elastic Curve from the Load Distribution

180
The structural behavior of beams under distributed loads is critical for engineering analysis, which focuses on predicting how beams bend and react under such conditions. Different types of beams (e.g., cantilever, supported, or overhanging) behave differently under distributed load conditions.
For all beams, the analysis of the beam's reaction to distributed loads begins by understanding the relationship between a beam's load and the resulting shear forces and bending moments.
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Load-frequency control01:28

Load-frequency control

165
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
165
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

116
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Load balance -aware dynamic cloud-edge-end collaborative offloading strategy.

Yueqi Fan1

  • 1Shanxi Polytechnic College, Taiyuan, Shanxi, China.

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|January 12, 2024
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Summary
This summary is machine-generated.

This study introduces a dynamic load balance-aware offloading strategy for cloud-edge-end (CEE) computing. The novel approach optimizes resource utilization and minimizes latency in CEE systems.

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Cloud Computing

Background:

  • Cloud-edge-end (CEE) computing integrates edge and cloud paradigms.
  • Efficient offloading strategies are crucial for CEE system collaboration.
  • Existing CEE offloading research often overlooks load balancing for edge resource utilization.

Purpose of the Study:

  • To develop a dynamic load balance-aware offloading strategy for CEE systems.
  • To address the research gap concerning load balance in CEE offloading.
  • To minimize latency while ensuring full utilization of edge resources.

Main Methods:

  • Proposed a load evolution model to analyze offloading impacts on system load dynamics.
  • Established a latency model as a performance metric for offloading strategies.
  • Formulated an optimal control model to find the best offloading strategy.
  • Developed a genetic algorithm-based numerical method to solve the optimal control model.

Main Results:

  • The proposed load evolution and latency models effectively characterize CEE system dynamics.
  • The genetic algorithm-based method is feasible and effective for solving the optimal control model.
  • Numerical experiments validated the effectiveness of the proposed dynamic load balance-aware offloading strategy.

Conclusions:

  • The developed strategy successfully incorporates dynamic load balancing into CEE offloading.
  • The approach optimizes CEE systems by minimizing latency and maximizing edge resource utilization.
  • This work provides a significant advancement in CEE system design and performance optimization.