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, Volume 18, Issue 6, pp 1737–1757 | Cite as

Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments

  • Fahimeh RamezaniEmail author
  • Jie Lu
  • Javid Taheri
  • Farookh Khadeer Hussain
Article

Abstract

Optimizing task scheduling in a distributed heterogeneous computing environment, which is a nonlinear multi-objective NP-hard problem, plays a critical role in decreasing service response time and cost, and boosting Quality of Service (QoS). This paper, considers four conflicting objectives, namely minimizing task transfer time, task execution cost, power consumption, and task queue length, to develop a comprehensive multi-objective optimization model for task scheduling. This model reduces costs from both the customer and provider perspectives by considering execution and power cost. We evaluate our model by applying two multi-objective evolutionary algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Genetic Algorithm (MOGA). To implement the proposed model, we extend the Cloudsim toolkit by using MOPSO and MOGA as its task scheduling algorithms which determine the optimal task arrangement among VMs. The simulation results show that the proposed multi-objective model finds optimal trade-off solutions amongst the four conflicting objectives, which significantly reduces the job response time and makespan. This model not only increases QoS but also decreases the cost to providers. From our experimentation results, we find that MOPSO is a faster and more accurate evolutionary algorithm than MOGA for solving such problems.

Keywords

Cloud computing Task scheduling Multi-objective particle swarm optimization Multi-objective genetic algorithm Jswarm Cloudsim 

Notes

Acknowledgments

The work presented in this paper was supported by the Australian Research Council (ARC) under Discovery Project DP140101366. The Authors also would like to thank Mr Chaosong Nie for his kind help in implementing the MOGA algorithm.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Fahimeh Ramezani
    • 1
    Email author
  • Jie Lu
    • 1
  • Javid Taheri
    • 2
  • Farookh Khadeer Hussain
    • 1
  1. 1.Decision Support and e-Service Intelligence Lab, Centre for Quantum Computation & Intelligent Systems, School of Software, Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydneyAustralia
  2. 2.Center for Distributed and High Performance Computing, School of Information TechnologiesUniversity of SydneySydneyAustralia

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