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Ampere-Maxwell's Law: Problem-Solving01:17

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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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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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Updated: Jun 11, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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AI-powered VM selection: Amplifying cloud performance with dragonfly algorithm.

Sindhu Rashmi1, Vikas Siwach1, Harkesh Sehrawat1

  • 1UIET, MDU Rohtak, India.

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PubMed
Summary
This summary is machine-generated.

This study introduces the Dragonfly Algorithm integrated with Modified Best Fit Decreasing (DA-MBFD) for efficient virtual machine placement in cloud computing. DA-MBFD significantly reduces power consumption and virtual machine migrations.

Keywords:
Cloud computingCloud data centerCloud service providerModified best fit decreasingPhysical machineVirtual machine

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Area of Science:

  • Cloud Computing
  • Virtualization Technology
  • Resource Management

Background:

  • Cloud computing's popularity necessitates efficient resource management.
  • Virtualization in cloud environments requires careful handling of resources and energy.
  • Increasing virtual machine (VM) requests challenge cloud data centers.

Purpose of the Study:

  • To propose a novel algorithm for optimizing VM placement.
  • To minimize power consumption and VM migration counts in cloud data centers.
  • To enhance the efficiency of cloud resource utilization.

Main Methods:

  • Introduced the Dragonfly Algorithm integrated with Modified Best Fit Decreasing (DA-MBFD).
  • Utilized Modified Best Fit Decreasing (MBFD) for VM resource requirement ranking.
  • Employed Minimization of Migration (MM) for hotspot detection and Dragonfly Algorithm (DA) for VM replacement optimization.

Main Results:

  • DA-MBFD demonstrated significant reductions in power consumption (up to 8.6%).
  • Achieved notable decreases in Service Level Agreement (SLA) violations (up to 9.25%).
  • Showcased a reduction in the number of VM migrations (up to 8.92%).

Conclusions:

  • DA-MBFD offers superior performance compared to existing VM placement techniques.
  • The proposed algorithm effectively balances resource utilization and energy efficiency.
  • DA-MBFD presents a promising solution for managing VM placement in large-scale cloud environments.