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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow

Xuejun Li1, Jia Xu2, Yun Yang2

  • 1Key Laboratory of ICSP, Ministry of Education, Anhui University, Hefei 230039, China ; School of Computer Science and Technology, Anhui University, Hefei 230601, China.

Computational Intelligence and Neuroscience
|September 11, 2015
PubMed
Summary

This study introduces a Chaotic Particle Swarm Optimization (CPSO) algorithm for cloud workflow task scheduling. CPSO effectively reduces costs by avoiding premature convergence, outperforming traditional Ant Colony Optimization and Particle Swarm Optimization methods.

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Optimization Algorithms

Background:

  • Cloud workflow systems automate applications, with task-level scheduling being a key optimization challenge.
  • Existing methods like Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) struggle with premature convergence, limiting cost reduction.
  • Market-oriented business models are significant factors in cloud workflow systems.

Purpose of the Study:

  • To address the limitations of ACO and PSO in cloud workflow task scheduling.
  • To propose an optimized task-level scheduling method that effectively reduces costs.
  • To enhance solution diversity and global convergence in scheduling algorithms.

Main Methods:

  • Implementation of a Chaotic Particle Swarm Optimization (CPSO) algorithm for task-level scheduling.
  • Integration of chaotic sequences to improve solution diversity and global convergence.
  • Application of an adaptive inertia weight factor to balance exploration and avoid premature convergence.

Main Results:

  • The proposed CPSO algorithm demonstrated lower costs compared to ACO and PSO.
  • Chaotic sequences enhanced solution diversity and ensured good global convergence.
  • The adaptive inertia weight factor successfully prevented premature convergence.

Conclusions:

  • CPSO offers a superior approach to task-level scheduling in cloud workflow systems.
  • The algorithm effectively balances global and local exploration for optimal cost reduction.
  • This method provides a more efficient solution for cloud workflow automation.