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An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud Computing.

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  • 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

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Summary
This summary is machine-generated.

This study introduces HLWOA, a novel algorithm for cloud computing workflow scheduling. HLWOA effectively minimizes task completion time and execution costs, outperforming existing methods for better resource allocation.

Keywords:
HEFTLévy flightWOAmulti-objective optimizationworkflow scheduling

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Area of Science:

  • Cloud Computing
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Workflow scheduling in cloud computing is crucial for efficient task execution.
  • Existing algorithms struggle to simultaneously optimize completion time and cost while meeting Quality of Service (QoS) requirements.
  • Developing advanced scheduling strategies remains an active research area.

Purpose of the Study:

  • To propose a hybrid QoS-aware workflow scheduling algorithm (HLWOA) for cloud computing.
  • To address the multi-objective optimization problem of minimizing both makespan (completion time) and execution cost.
  • To enhance resource allocation efficiency in cloud data centers.

Main Methods:

  • Modeling the workflow scheduling problem as a multi-objective optimization task.
  • Utilizing the Heterogeneous Earliest Finish Time (HEFT) algorithm for initial task sequencing.
  • Developing an improved Whale Optimization Algorithm (WOA) incorporating Lévy flight for enhanced optimization.
  • Using HEFT's output as an initial population for WOA to speed up convergence.
  • Implementing a Lévy flight strategy to prevent local optima during iterative optimization.

Main Results:

  • HLWOA demonstrated superior performance compared to HEFT, HEPGA, and standard WOA.
  • The algorithm achieved significant reductions in both makespan and execution cost.
  • Evaluations on real-world scientific workflows (Cybershake, Montage) confirmed HLWOA's effectiveness across different scales.
  • Normalized fitness values consistently ranked first, indicating overall performance superiority.

Conclusions:

  • HLWOA is an effective hybrid algorithm for QoS-aware workflow scheduling in cloud environments.
  • The proposed algorithm successfully balances the trade-offs between completion time and execution cost.
  • HLWOA offers a promising solution for optimizing resource utilization and task execution in cloud computing platforms.