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Soft Computing

, Volume 23, Issue 21, pp 11035–11054 | Cite as

A novel task scheduling scheme in a cloud computing environment using hybrid biogeography-based optimization

  • Zhao TongEmail author
  • Hongjian Chen
  • Xiaomei Deng
  • Kenli Li
  • Keqin Li
Methodologies and Application
  • 139 Downloads

Abstract

Task scheduling, which plays a crucial role in cloud computing and is the critical factor influencing the performance of cloud computing, is an NP-hard problem that can be solved with a heuristic algorithm. In this paper, we propose a novel heuristic algorithm, called biogeography-based optimization (BBO), and a new hybrid migrating BBO (HMBBO) algorithm, which integrates the migration strategy with particle swarm optimization (PSO). Both methods are proposed to solve the problem of scheduling-directed acyclic graph tasks in a cloud computing environment. The basic idea of our approach is to exploit the advantages of the PSO and BBO algorithms while avoiding their drawbacks. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. Both simulation and real-life experiments are conducted to verify the effectiveness of HMBBO. The experiment shows that compared with several classic heuristic algorithms, HMBBO has advantages in terms of global search ability, fast convergence rate and a high-quality solution, and it provides a new method for task scheduling in cloud computing.

Keywords

Biogeography-based optimization Cloud computing Directed acyclic graph Task scheduling WorkflowSim 

Notes

Acknowledgements

The research was partially funded by the Program of National Natural Science Foundation of China (Grant No. 61502165), the National Outstanding Youth Science Program of National Natural Science Foundation of China (Grant No. 61625202).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information Science and EngineeringHunan Normal UniversityChangshaChina
  2. 2.College of Information Science and EngineeringHunan UniversityChangshaChina
  3. 3.National Supercomputing Center in ChangshaChangshaChina
  4. 4.Department of Computer ScienceState University of New YorkNew PaltzUSA

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