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Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

603
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...
603
Distributed Loads01:19

Distributed Loads

487
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...
487
Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

579
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.
579
Elastic Curve from the Load Distribution01:16

Elastic Curve from the Load Distribution

147
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.
147
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

143
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
143
Resultant of a General Distributed Loading01:13

Resultant of a General Distributed Loading

619
While designing structures exposed to non-uniform loads, it is crucial to consider the resultant force and its location. This resultant force is a single vector representing the net force applied due to the distributed load.
Examples such as load distribution due to wind and load distribution on a bridge illustrate how this concept is used to analyze and design safe, reliable structures under variable loading conditions. Most structures, such as residential buildings, bridges, and towers, are...
619

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Updated: May 20, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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DE-RALBA: dynamic enhanced resource aware load balancing algorithm for cloud computing.

Altaf Hussain1, Muhammad Aleem2, Atiq Ur Rehman1

  • 1Department of Computer Science, KICSIT Campus, Institute of Space Technology, Islamabad, Pakistan.

Peerj. Computer Science
|March 26, 2025
PubMed
Summary
This summary is machine-generated.

A new dynamic algorithm, DE-RALBA, improves resource utilization and reduces job completion time for high-performance computing (HPC) applications in cloud environments by balancing workloads across virtual machines.

Keywords:
Cloud computingDE-RALBADistributed computingDynamic load balancingDynamic schedulingLoad balancingResource-aware load balancingResource-aware schedulingScheduling algorithm

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Area of Science:

  • Cloud Computing
  • High-Performance Computing (HPC)
  • Resource Management

Background:

  • Cloud computing offers scalable resources for HPC applications.
  • Inefficient resource utilization stems from workload imbalance in heterogeneous environments.
  • Static scheduling leads to poor resource use and longer completion times.

Purpose of the Study:

  • To propose a dynamic enhanced resource-aware load balancing algorithm (DE-RALBA).
  • To mitigate load imbalance in job scheduling for cloud computing.
  • To improve makespan and resource utilization for HPC applications.

Main Methods:

  • Developed the dynamic enhanced resource-aware load balancing algorithm (DE-RALBA).
  • Evaluated DE-RALBA using the CloudSim simulator.
  • Tested with heterogeneous computing scheduling problems (HCSP) and Google Cloud Jobs (GoCJ) datasets.

Main Results:

  • DE-RALBA effectively mitigates load imbalance in cloud job scheduling.
  • Achieved up to 52.35% improved resource utilization on HCSP instances.
  • Demonstrated superior resource utilization on the GoCJ dataset compared to existing algorithms.

Conclusions:

  • DE-RALBA significantly improves makespan and resource utilization.
  • The algorithm offers a practical solution for efficient HPC workload management in clouds.
  • Dynamic scheduling, with enhancements like DE-RALBA, is crucial for cloud resource optimization.