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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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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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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:
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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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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive

D Chitra Devi1, V Rhymend Uthariaraj1

  • 1Ramanujam Computing Centre, Anna University, Chennai 600 025, India.

Thescientificworldjournal
|March 9, 2016
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Summary
This summary is machine-generated.

This study introduces a novel scheduling and load balancing algorithm for cloud computing. It optimizes task placement on virtual machines (VMs) by considering VM capabilities, task length, and interdependencies for improved efficiency.

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Resource Management

Background:

  • Cloud computing relies on scheduling and load balancing for efficient resource sharing via virtual machines (VMs).
  • Non-preemptive task scheduling in cloud environments requires accurate initial VM placement due to its irreversible nature.
  • Job arrivals are dynamic, featuring interdependent tasks and varying intervals under diverse load conditions.

Purpose of the Study:

  • To introduce and evaluate a new scheduling and load balancing algorithm for cloud computing.
  • To enhance cloud efficiency and user satisfaction by optimizing task allocation.
  • To address the challenges of dynamic job arrivals and task interdependencies.

Main Methods:

  • Developing a novel algorithm that considers VM capabilities, task length, and task interdependencies.
  • Implementing dynamic scheduling and load balancing strategies.
  • Comparing the proposed algorithm's performance against existing methods.

Main Results:

  • The proposed algorithm demonstrates improved efficiency in cloud computing resource management.
  • Effective task migration to underutilized virtual machines (VMs) is achieved.
  • Consideration of task interdependencies leads to better allocation decisions.

Conclusions:

  • The developed scheduling and load balancing algorithm offers a more efficient approach to cloud resource management.
  • The algorithm's effectiveness is validated through comparative performance analysis.
  • Optimizing task placement based on VM capabilities and task characteristics enhances overall system performance and user satisfaction.