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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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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Maxwell-Boltzmann Distribution: Problem Solving01:20

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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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Parallel Processing01:20

Parallel Processing

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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...
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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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Related Experiment Video

Updated: Jan 10, 2026

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

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Load balancing for cloud computing using optimized cluster based federated learning.

Krishna Keerthi Chennam1, Uma Maheswari V2, Rajanikanth Aluvalu3

  • 1Department of CSE, Vasavi College of Engineering, Hyderabad, India.

Scientific Reports
|November 21, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Cluster-based Federated Learning (FL) framework for efficient cloud task scheduling and load balancing. The new model optimizes resource utilization, reducing execution times and energy consumption.

Keywords:
Cloud computingCluster-based federated learningLoad balancingUser’s taskVirtual machines

Related Experiment Videos

Last Updated: Jan 10, 2026

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

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Area of Science:

  • Cloud Computing
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Cloud computing faces NP-hard optimization challenges in task scheduling and load balancing.
  • Inefficient resource utilization, high energy consumption, and long execution times are common issues.

Purpose of the Study:

  • To develop a novel Cluster-based Federated Learning (FL) framework for efficient cloud task scheduling and load balancing.
  • To address system heterogeneity by clustering virtual machines (VMs) with similar characteristics.

Main Methods:

  • Implemented unsupervised learning for VM clustering based on characteristics.
  • Utilized VM capabilities and a derivative-based objective function for optimized scheduling.
  • Benchmarked against Whale Optimization Algorithm (WOA), Butterfly Optimization (BFO), Mayfly Optimization (MFO), and Fire Hawk Optimization (FHO).

Main Results:

  • The Cluster-based FL model with the COA algorithm demonstrated superior performance.
  • Achieved up to a 10% reduction in makespan and a 15% decrease in idle time.
  • Showcased significant improvements in load balancing across virtual machines.

Conclusions:

  • The integration of clustering within federated learning offers a scalable and adaptive solution for cloud resource management.
  • The proposed framework provides a resilient approach to optimizing cloud environments.
  • This method effectively enhances efficiency and reduces resource wastage in cloud task scheduling.