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An Optimized Framework for Energy-Resource Allocation in A Cloud Environment based on the Whale Optimization

Shanky Goyal1,2, Shashi Bhushan2, Yogesh Kumar3

  • 1Research Scholar, CSE Department, IKGPTU, Jalandhar 144603, India.

Sensors (Basel, Switzerland)
|March 6, 2021
PubMed
Summary
This summary is machine-generated.

This study optimized cloud computing efficiency by comparing load balancing algorithms. The whale optimization algorithm demonstrated superior performance in reducing energy consumption and improving server resource scheduling.

Keywords:
cloud computingenergy efficiencyload balancingpower consumptionresource schedulingwhale optimization

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Optimization Algorithms

Background:

  • Cloud computing enables remote data access and manipulation, offering an innovative alternative to physical infrastructure investments.
  • Increasingly, cloud infrastructure faces challenges related to high energy consumption and the need for efficient resource scheduling.
  • Effective load balancing is crucial for distributing workloads across servers to meet escalating user demands.

Purpose of the Study:

  • To investigate and compare the effectiveness of various optimization algorithms for enhancing cloud computing environments.
  • To address the critical issues of energy consumption and resource scheduling within cloud infrastructure.
  • To evaluate the performance of load balancing techniques for optimizing server effectiveness.

Main Methods:

  • The study employed several optimization algorithms: Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), BAT, Cuckoo Search Algorithm (CSA), and Whale Optimization Algorithm (WOA).
  • These algorithms were applied to optimize load balancing, energy efficiency, and resource scheduling in a cloud environment.
  • Performance was evaluated using configurations with seven and eight servers.

Main Results:

  • The Whale Optimization Algorithm (WOA) demonstrated superior performance compared to other tested algorithms.
  • WOA achieved better results in terms of reduced response time and energy consumption.
  • The algorithm also showed improvements in execution time and throughput, indicating enhanced overall efficiency.

Conclusions:

  • The Whale Optimization Algorithm is highly effective for load balancing and resource scheduling in cloud computing.
  • Implementing WOA can lead to significant reductions in energy consumption for cloud infrastructure.
  • This optimization approach contributes to creating more efficient and responsive cloud environments.