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Task Scheduling Optimization in Cloud Computing Applying Multi-Objective Particle Swarm Optimization

  • Fahimeh Ramezani
  • Jie Lu
  • Farookh Hussain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8274)

Abstract

Optimizing the scheduling of tasks in a distributed heterogeneous computing environment is a nonlinear multi-objective NP-hard problem which is playing an important role in optimizing cloud utilization and Quality of Service (QoS). In this paper, we develop a comprehensive multi-objective model for optimizing task scheduling to minimize task execution time, task transferring time, and task execution cost. However, the objective functions in this model are in conflict with one another. Considering this fact and the supremacy of Particle Swarm Optimization (PSO) algorithm in speed and accuracy, we design a multi-objective algorithm based on multi-objective PSO (MOPSO) method to provide an optimal solution for the proposed model. To implement and evaluate the proposed model, we extend Jswarm package to multi-objective Jswarm (MO-Jswarm) package. We also extend Cloudsim toolkit applying MO-Jswarm as its task scheduling algorithm. MO-Jswarm in Cloudsim determines the optimal task arrangement among VMs according to MOPSO algorithm. The simulation results show that the proposed method has the ability to find optimal trade-off solutions for multi-objective task scheduling problems that represent the best possible compromises among the conflicting objectives, and significantly increases the QoS.

Keywords

Cloud computing Task Scheduling Multi-Objective Particle Swarm Optimization Jswarm Cloudsim 

References

  1. 1.
    Celesti, A., Fazio, M., Villari, M., Puliafito, A.: Virtual machine provisioning through satellite communications in federated cloud environments. Future Generation Computer Systems 28(1), 85–93 (2012)CrossRefGoogle Scholar
  2. 2.
    Xiao, Z.J., Chang, H.Y., Yi, Y.: An optimization m ethod of w orkflow dynamic scheduling based on heuristic GA. Computer Science 34(2) (2007)Google Scholar
  3. 3.
    Zomaya, A.Y., Yee-Hwei, T.: Observations on using genetic algorithms for dynamic load-balancing. IEEE Transactions on Parallel and Distributed Systems 12(9), 899–911 (2001)CrossRefGoogle Scholar
  4. 4.
    Juhnke, E., Dörnemann, T., Böck, D., Freisleben, B.: Multi-objective scheduling of bpel workflows in geographically distributed clouds. In: 4th IEEE International Conference on Cloud Computing, pp. 412–419 (2011)Google Scholar
  5. 5.
    Salman, A., Ahmad, I., Al-Madani, S.: Particle swarm optimization for task assignment problem. Microprocessors and Microsystems 26(8), 363–371 (2002)CrossRefGoogle Scholar
  6. 6.
    Lei, Z., Yuehui, C., Runyuan, S., Shan, J., Bo, Y.: A task scheduling algorithm based on pso for grid computing. International Journal of Computational Intelligence Research 4(1), 37–43 (2008)Google Scholar
  7. 7.
    Guo, L., Zhao, S., Shen, S., Jiang, C.: Task scheduling optimization in cloud computing based on heuristic algorithm. Journal of Networks 7(3), 547–553 (2012)CrossRefGoogle Scholar
  8. 8.
    Liu, H., Abraham, A., Snášel, V., McLoone, S.: Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments. Information Sciences 192(0), 228–243 (2012)CrossRefGoogle Scholar
  9. 9.
    Behbood, V., Lu, J., Zhang, G.: Fuzzy bridged refinement domain adaptation: Long-term bank failure prediction. International Journal of Computational Intelligence and Applications 12(01) (2013), doi:10.1142/S146902681350003XGoogle Scholar
  10. 10.
  11. 11.
    Calheiros, R.N., Ranjan, R., De Rose, C.A.F., Buyya, R.: Cloudsim: A novel framework for modeling and simulation of cloud computing infrastructures and services. Arxiv preprint arXiv:0903.2525 (2009)Google Scholar
  12. 12.
    Song, B., Hassan, M.M., Huh, E.: A novel heuristic-based task selection and allocation framework in dynamic collaborative cloud service platform. In: 2nd IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 360–367 (2010)Google Scholar
  13. 13.
    Li, J., Peng, J., Cao, X., Li, H.-y.: A task scheduling algorithm based on improved ant colony optimization in cloud computing environment. Energy Procedia 13, 6833–6840 (2011)CrossRefGoogle Scholar
  14. 14.
    Li, J., Qiu, M., Ming, Z., Quan, G., Qin, X., Gu, Z.: Online optimization for scheduling preemptable tasks on iaas cloud systems. Journal of Parallel and Distributed Computing 72(5), 666–677 (2012)CrossRefGoogle Scholar
  15. 15.
    Tayal, S.: Tasks scheduling optimization for the cloud computing systems. International Journal of Advanced Engineering Sciences and Technologies 5(2), 111–115 (2011)MathSciNetGoogle Scholar
  16. 16.
    Chen, Y.M., Tsai, S.Y.: Optimal provisioning of resource in a cloud service. IJCSI International Journal of Computer Science Issues 7(6), 1694–1814 (2010)MathSciNetGoogle Scholar
  17. 17.
    Mahmoodabadi, M.J., Bagheri, A., Nariman-zadeh, N., Jamali, A.: A new optimization algorithm based on a combination of particle swarm optimization, convergence and divergence operators for single-objective and multi-objective problems. Engineering Optimization, 1–20 (2012)Google Scholar
  18. 18.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  19. 19.
    Alves, M.J.: Using MOPSO to solve multiobjective bilevel linear problems. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Engelbrecht, A.P., Groß, R., Stützle, T. (eds.) ANTS 2012. LNCS, vol. 7461, pp. 332–339. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    Gao, Y., Zhang, G., Lu, J., Wee, H.-M.: Particle swarm optimization for bi-level pricing problems in supply chains. Journal of Global Optimization 51(2), 245–254 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Lu, J., Zhang, G., Ruan, D.: Multi-objective group decision making: Methods, software and applications with fuzzy set techniques. Imperial College Press, London (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fahimeh Ramezani
    • 1
  • Jie Lu
    • 1
  • Farookh Hussain
    • 1
  1. 1.Decision Systems & e-Service Intelligence Lab, Centre for Quantum Computation & Intelligent Systems School of Software, Faculty of Engineering and Information TechnologyUniversity of Technology, SydneyBroadwayAustralia

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