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Related Concept Videos

Distributed Loads01:19

Distributed Loads

743
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...
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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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...
868
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

255
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.
255
Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

896
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.
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Elastic Curve from the Load Distribution01:16

Elastic Curve from the Load Distribution

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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. Initially, this...
327
Load-frequency control01:28

Load-frequency control

309
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...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Enhancing Mobile Edge Computing with Efficient Load Balancing Using Load Estimation in Ultra-Dense Network.

Wen Chen1, Yongqi Zhu1, Jiawei Liu1

  • 1School of Information Science and Technology, Donghua University, Shanghai 201620, China.

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|May 5, 2021
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Summary
This summary is machine-generated.

This study introduces a novel load balancing algorithm for mobile edge computing (MEC) within ultra-dense networks (UDN). The algorithm uses user load prediction to optimize task offloading, reducing network load imbalance and the ping-pong effect.

Keywords:
genetic algorithm (GA)load balancingmobile edge computing (MEC)ping-pong effectsoftware defined network (SDN)subtaskultra dense network (UDN)

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Area of Science:

  • Computer Science
  • Network Engineering

Background:

  • Exponential growth in mobile devices creates demand for computing resources.
  • Mobile Edge Computing (MEC) and Ultra-Dense Networks (UDN) offer solutions.
  • Load imbalance in MEC servers degrades network performance.

Purpose of the Study:

  • To address task allocation challenges in MEC/UDN environments.
  • To propose a load balancing algorithm mitigating load imbalance.
  • To optimize task offloading for improved network stability.

Main Methods:

  • Application of Software-Defined Networking (SDN) for task allocation.
  • Development of a load balancing algorithm using user load prediction.
  • Utilizing a Genetic Algorithm (GA) to validate algorithm effectiveness.

Main Results:

  • The proposed algorithm effectively balances load among MEC servers.
  • It significantly reduces the ping-pong effect common in load balancing.
  • Demonstrates efficiency in optimizing systematic stability.

Conclusions:

  • The novel load balancing algorithm enhances MEC/UDN performance.
  • User load prediction is key to mitigating load imbalance.
  • The method offers a robust solution for dynamic task offloading.