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Related Concept Videos

Distributed Loads01:19

Distributed Loads

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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.
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

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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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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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In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
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Beams with Symmetric Loadings01:15

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The moment-area method is an analytical tool used in structural engineering to determine the slope and deflection of beams under various loads. Consider a cantilever with a concentrated load and moment at the free end. The first step is constructing a free-body diagram to calculate the reactions at the fixed end. Next, the bending moment diagram is plotted to visualize how the bending moment varies along the beam's length, focusing on points where the bending moment equals zero.
The M/EI...
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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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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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SJFO: Sail Jelly Fish Optimization enabled VM migration with DRNN-based prediction for load balancing in cloud

Rajesh Rathinam1, Premkumar Sivakumar2, Sivakumar Sigamani3

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.

Network (Bristol, England)
|June 3, 2024
PubMed
Summary

This study introduces the Sail Jelly Fish Optimization (SJFO) algorithm for efficient virtual machine (VM) migration in cloud computing. SJFO optimizes load balancing, enhancing cloud resource management and performance.

Keywords:
Cloud computingDeep Recurrent Neural Network (DRNN)SailFish Optimizer (SFO)Virtual Machine (VM)

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Area of Science:

  • Cloud Computing
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Dynamic workloads in cloud environments necessitate efficient load balancing strategies.
  • Virtual Machine (VM) migration is a key technique for managing resource distribution across physical machines (PMs).
  • Existing load balancing methods require enhancement for optimal performance in complex cloud architectures.

Purpose of the Study:

  • To propose a novel optimization algorithm, Sail Jelly Fish Optimization (SJFO), for VM migration-based load balancing.
  • To enhance the efficiency of load distribution and resource utilization in cloud data centers.
  • To evaluate the performance of the proposed SJFO algorithm in managing dynamic workloads.

Main Methods:

  • Developed SJFO by combining Sail Fish Optimizer (SFO) and Jellyfish Search (JS) algorithms.
  • Integrated Deep Recurrent Neural Network (DRNN) for accurate load prediction.
  • Implemented VM migration triggered by predicted load exceeding a defined threshold.

Main Results:

  • The SJFO algorithm demonstrated superior performance in load balancing.
  • Achieved a superior capacity metric of 0.598.
  • Showcased an inferior load metric of 0.089 and an inferior resource utilization metric of 0.257, indicating improved efficiency.

Conclusions:

  • The proposed SJFO-VM migration strategy effectively balances dynamic workloads in cloud environments.
  • SJFO offers a significant improvement over existing methods for cloud resource management.
  • The integration of DRNN for load prediction further enhances the effectiveness of the proposed solution.