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GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud-Whale Optimization in Cloud Computing.

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This study introduces a novel whale-Gaussian cloud optimization strategy for efficient cloud computing task scheduling. The method enhances virtual machine resource utilization, reduces task completion time, and balances loads for cost reduction.

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Area of Science:

  • Cloud Computing
  • Artificial Intelligence
  • Operations Research

Background:

  • Efficiently managing millions of user requests is a key challenge in cloud computing.
  • Task scheduling significantly impacts resource configuration and operational costs.
  • Task and resource scheduling in cloud environments is an NP-hard problem.

Purpose of the Study:

  • To propose a novel three-layer scheduling model for cloud computing task scheduling.
  • To optimize virtual machine resource utilization and minimize task completion time.
  • To achieve load balancing across virtual machines and reduce system operating costs.

Main Methods:

  • A three-layer scheduling model integrating whale optimization and Gaussian cloud concepts.
  • A whale optimization strategy based on the Gaussian cloud model (GCWOAS2) for multi-objective task scheduling.
  • Incorporation of opposition-based learning and an adaptive mobility factor to enhance search capabilities and avoid local optima.

Main Results:

  • The proposed Gaussian whale-cloud optimization (GCWOA) strategy effectively expands the search range and achieves global optimal solutions.
  • Experimental results demonstrate significant improvements in shortening task completion time compared to existing metaheuristic algorithms.
  • The strategy successfully balances the load of virtual machine resources and improves overall resource utilization.

Conclusions:

  • The GCWOA strategy offers a superior approach to multi-objective task scheduling in cloud computing.
  • This method provides a practical solution for enhancing efficiency and reducing costs in cloud environments.
  • The proposed model shows promising performance in resource utilization and load balancing for virtual machines.