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A Hybrid SAO and RIME Optimizer for Global Optimization and Cloud Task Scheduling.

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A new hybrid optimizer (HSAO) improves cloud computing task scheduling by enhancing exploration and convergence. This method effectively addresses latency-sensitive tasks, outperforming existing algorithms in simulations and real-world applications.

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IEEE CEC2017RIME optimization algorithmcloud task schedulingcost optimizationsnow ablation optimizer

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • The digital economy heavily relies on cloud computing, with tasks increasingly demanding low latency.
  • Latency-sensitive computational tasks present significant challenges for existing cloud task scheduling methods.
  • Effective scheduling is crucial for optimizing cloud resource utilization and performance.

Purpose of the Study:

  • To develop an advanced optimization algorithm for efficient cloud computing task scheduling.
  • To address the limitations of current methods in handling latency-sensitive tasks.
  • To improve the exploration and convergence capabilities of optimization algorithms in complex scheduling problems.

Main Methods:

  • Proposed a hybrid Sine-Cosine Algorithm and RIME (SAO and RIME) optimizer (HSAO).
  • Incorporated ecological niche differentiation for population initialization in SAO.
  • Integrated RIME's soft frost search and hard frost piercing mechanisms for enhanced local optima escape and convergence.
  • Implemented a population-based collaborative boundary control method to manage outlier individuals.

Main Results:

  • The HSAO algorithm demonstrated significant advantages over 11 other algorithms on the IEEE CEC2017 test set.
  • Statistical analysis confirmed the superior performance of HSAO in global optimization tasks.
  • HSAO achieved excellent results when applied to real-world cloud computing task scheduling scenarios.

Conclusions:

  • The proposed HSAO algorithm is effective for global optimization and cloud task scheduling.
  • HSAO successfully addresses the challenge of scheduling latency-sensitive cloud computing tasks.
  • The algorithm shows practical applicability and potential for real-world cloud environments.