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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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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A multi-objective approach to load balancing in cloud environments integrating ACO and WWO techniques.

Umesh Kumar Lilhore1, Sarita Simaiya2,3, Yogendra Narayan Prajapati4

  • 1School of Computing Science and Engineering, Galgotias University, Greater Noida, UP, India.

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|April 8, 2025
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Summary

This study introduces a hybrid optimization method combining Water Wave Optimization (WWO) and Ant Colony Optimization (ACO) for efficient cloud computing resource allocation. The WWO-ACO approach significantly improves task scheduling, reduces costs, and lowers energy consumption.

Keywords:
Ant colony optimizationCloud load balancingHybrid optimizationResource allocationWater wave optimization

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Area of Science:

  • Cloud Computing
  • Optimization Algorithms
  • Resource Management

Background:

  • Dynamic cloud environments necessitate efficient load balancing and resource allocation.
  • Increasing demand for rapid and continuous service delivery poses significant challenges.
  • Existing optimization methods may not fully address the complexities of modern cloud infrastructures.

Purpose of the Study:

  • To introduce a novel hybrid optimization method combining Water Wave Optimization (WWO) and Ant Colony Optimization (ACO).
  • To enhance load balancing and resource allocation in dynamic cloud computing environments.
  • To improve key performance indicators including response times, resource efficiency, and operational costs.

Main Methods:

  • A hybrid optimization approach integrating WWO for global search and ACO for local search.
  • Extensive simulations using a cloud-sim simulator and diverse workload trace files.
  • Comparative analysis against established algorithms like WWO, Genetic Algorithm (GA), Spider Monkey Optimization (SMO), and ACO.

Main Results:

  • The hybrid WWO-ACO approach improved task scheduling efficiency by 11%.
  • Operational expenses were reduced by 8%, and energy usage decreased by 12% compared to conventional methods.
  • Achieved consistent resource allocation balance values between 0.87 and 0.95.

Conclusions:

  • The hybrid WWO-ACO algorithm demonstrates substantial improvements in cloud computing optimization.
  • The method effectively enhances system performance and user satisfaction.
  • This hybrid approach offers a significant advancement in cloud resource management techniques.