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Related Concept Videos

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...
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Distributed Loads01:19

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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.
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Relation Between the Distributed Load and Shear01:23

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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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Load-frequency control

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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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Organisms must balance energy intake with the energy required for growth, maintenance and reproduction. These trade-offs result in a variety of survivorship and reproductive strategies, including semelparity and iteroparity. Semelparous species, like annual plants, have only one reproductive episode in their lifetimes and consequently have short lifespans. Iteroparous species, by contrast, have many reproductive events during their lifetimes but have relatively few offspring. These two...
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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).
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Optimal Resource Provisioning and Task Offloading for Network-Aware and Federated Edge Computing.

Avilia Kusumaputeri Nugroho1, Shigeo Shioda2, Taewoon Kim1

  • 1School of Computer Science and Engineering, Pusan National University, Busan 46241, Republic of Korea.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
Summary
This summary is machine-generated.

Mobile edge computing (MEC) offers better performance for delay-sensitive apps. Our NAFEOS solution optimizes user association and resource scaling for federated edge servers, improving utilization.

Keywords:
horizontal scalingmobile edge computingoptimal associationtask offloadingvertical scaling

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Network Engineering

Background:

  • Mobile Edge Computing (MEC) enhances responsiveness for delay-sensitive applications by processing tasks closer to users.
  • Optimal MEC utilization requires careful design in user association, resource provisioning, and task distribution.
  • The impact of federated edge servers on resource allocation and management is underexplored.

Purpose of the Study:

  • To address the network and MEC resource scheduling problem in federated edge environments.
  • To investigate the integration of network and MEC resource management for optimal performance.
  • To propose a solution that balances user association, federation assignment, and dynamic resource scaling.

Main Methods:

  • Developed NAFEOS, a two-stage algorithm for optimizing user-base station association and federation assignment.
  • Stage-1 optimizes user association and federation assignment for balanced edge server utilization.
  • Stage-2 dynamically schedules vertical and horizontal scaling to meet fluctuating task-offloading demands.

Main Results:

  • The proposed NAFEOS algorithm effectively integrates association optimization with vertical and horizontal scaling.
  • Stage-1 ensures balanced utilization of federated edge servers.
  • Stage-2 successfully manages dynamic resource scaling to fulfill user demands, achieving optimal resource utilization.

Conclusions:

  • NAFEOS provides an effective solution for network and MEC resource scheduling in federated environments.
  • The joint approach to network and MEC resource management is crucial for enhanced quality of service.
  • The study demonstrates significant improvements in resource utilization through optimized association and dynamic scaling.