Associate multi-task scheduling algorithm based on self-adaptive inertia weight particle swarm optimization with disruption operator and chaos operator in cloud environment

  • Rong Zhang
  • Feng Tian
  • Xiaochun Ren
  • Yaxing Chen
  • Kuoming Chao
  • Ruomeng Zhao
  • Bo Dong
  • Wei Wang
Original Research Paper
  • 16 Downloads

Abstract

Random search-based scheduling algorithms, such as particle swarm optimization (PSO), are often used to solve independent multi-task scheduling problems in cloud, but the quality of optimal solution of the algorithm often has greater deviation and poor stability when the tasks are associate. In this paper, we propose an algorithm called SADCPSO to solve this challenging problem, which improves the PSO algorithm by uniquely integrating the self-adaptive inertia weight, disruption operator and chaos operator. In particular, the self-adaptive inertia weight is adopted to adjust the convergence rate, the disruption operator is applied to prevent the loss of population diversity, and the chaos operator is introduced to prevent the solution from tending to jump into the local optimal. Furthermore, we also provide a scheme to apply the SADCPSO algorithm to solve the associate multi-task scheduling problem. In the simulation experiments, we initialize two associate multi-task scheduling examples and take the minimum execution time as our optimization objective. The simulation results demonstrate that the optimal solution of our proposed algorithm has better quality and stability than the baseline PSO algorithm.

Keywords

Cloud computing Associate task Disruption Chaos Particle swarm optimization 

Notes

Acknowledgements

It is a project supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000903, National Natural Science Foundation of China (61721002), Innovation Research Team of Ministry of Education (IRT_17R86), the National Natural Science Foundation of China under Grant Nos. 61472315, 61502379, 61428206, 61532015 and 61532004, the Project of China Knowledge Centre for Engineering Science and Technology.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Rong Zhang
    • 1
    • 2
  • Feng Tian
    • 2
  • Xiaochun Ren
    • 1
  • Yaxing Chen
    • 2
  • Kuoming Chao
    • 3
  • Ruomeng Zhao
    • 4
  • Bo Dong
    • 2
  • Wei Wang
    • 1
  1. 1.State Key Laboratory of Rail Transit Engineering Informatization (FSDI)Xi’anChina
  2. 2.Shaanxi Province Key Lab. of Satellite and Terrestrial Network Tech. R&DXi’an Jiaotong UniversityXi’anChina
  3. 3.School of Computing, Electronics and MathematicsCoventry UniversityCoventryUK
  4. 4.MacPractice Inc.LincolnUSA

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