World Wide Web

, 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


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.


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



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.


  1. 1.
    Alves, M.J.: Using MOPSO to solve multiobjective bilevel linear problems. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Buyya, R., Beloglazov, A., Abawajy, J.: Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges. arXiv preprint arXiv:1006.0308 (2010)Google Scholar
  3. 3.
    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
  4. 4.
    Cirne, W., et al.: Labs of the world, unite!!! J. Grid Comput. 4(3), 225–246 (2006)CrossRefzbMATHGoogle Scholar
  5. 5.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  6. 6.
    Gao, Y., Zhang, G., Lu, J., Wee, H.-M.: Particle swarm optimization for bi-level pricing problems in supply chains. J. Glob. Optim. 51(2), 245–254 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Guo, L., Zhao, S., Shen, S., Jiang, C.: Task scheduling optimization in cloud computing based on heuristic algorithm. J. Netw. 7(3), 547–553 (2012)Google Scholar
  8. 8.
    Hadka, D.: MOEA Framework A Free and Open Source Java Framework for Multiobjective Optimization, [Online], Available:
  9. 9.
    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
  10. 10.
    Lei, Z., Yuehui, C., Runyuan, S., Shan, J., Bo, Y.: A task scheduling algorithm based on PSO for grid computing. Int. J. Comput. Intell. Res. 4(1), 37–43 (2008)Google Scholar
  11. 11.
    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
  12. 12.
    Li, J., Qiu, M., Ming, Z., Quan, G., Qin, X., Gu, Z.: Online optimization for scheduling preemptable tasks on IaaS cloud systems. J. Parallel Distrib. Comput. 72(5), 666–677 (2012)CrossRefGoogle Scholar
  13. 13.
    Liu, H., Abraham, A., Snášel, V., McLoone, S.: Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments. Inf. Sci. 192, 228–243 (2012)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Mahabadi, A., Zahedi, S.M., Khonsari, A.: Reliable energy-aware application mapping and voltage–frequency island partitioning for GALS-based NoC. J. Comput. Syst. Sci. 79(4), 457–474 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    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. Eng. Optim. 44(10), 1–20 (2012)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Priya, B., Pilli, E.S., Joshi, R.C.: A survey on energy and power consumption models for Greener Cloud. In: Advance Computing Conference (IACC), 2013 I.E. 3rd International, 2013, IEEE, pp. 76–82Google Scholar
  18. 18.
    Ramezani, F., Lu, J., Hussain, F.K.: Task-based system load balancing in cloud computing using particle swarm optimization. Int. J. Parallel Prog. 42(5), 739–754 (2013)CrossRefGoogle Scholar
  19. 19.
    Ramezani, F., Lu, J., Hussain, F.: Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization. International Conference on Service Oriented Computing (ICSOC), pp. 237–251 (2013)Google Scholar
  20. 20.
    Rizvandi, N.B., Taheri, J., Zomaya, A.Y.: Some observations on optimal frequency selection in DVFS-based energy consumption minimization. J. Parallel Distrib. Comput. 71(8), 1154–1164 (2011)CrossRefzbMATHGoogle Scholar
  21. 21.
    Salman, A., Ahmad, I., Al-Madani, S.: Particle swarm optimization for task assignment problem. Microprocess. Microsyst. 26(8), 363–371 (2002)CrossRefGoogle Scholar
  22. 22.
    Shieh, W.-Y., Pong, C.-C.: Energy and transition-aware runtime task scheduling for multicore processors. J. Parallel Distrib. Comput. 73(9), 1225–1238 (2013)CrossRefGoogle Scholar
  23. 23.
    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
  24. 24.
    Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)CrossRefGoogle Scholar
  25. 25.
    Su, S., Li, J., Huang, Q., Huang, X., Shuang, K., Wang, J.: Cost-efficient task scheduling for executing large programs in the cloud. Parallel Comput. 39(4–5), 177–188 (2013)CrossRefGoogle Scholar
  26. 26.
    Taheri, J., Zomaya, A.Y., Bouvry, P., Khan, S.U.: Hopfield neural network for simultaneous job scheduling and data replication in grids. Futur. Gener. Comput. Syst. 29, 1885–1900 (2013)CrossRefGoogle Scholar
  27. 27.
    Taheri, J., Zomaya, A.Y., Siegel, H.J., Tari, Z.: Pareto frontier for job execution and data transfer time in hybrid clouds. Futur. Gener. Comput. Syst. 37, 321–334 (2014)CrossRefGoogle Scholar
  28. 28.
    Tayal, S.: Tasks scheduling optimization for the cloud computing systems. Int. J. Adv. Eng. Sci. Technol. 5(2), 111–115 (2011)MathSciNetGoogle Scholar
  29. 29.
    Tchernykh, A., Pecero, J.E., Barrondo, A., Schaeffer, E.: Adaptive energy efficient scheduling in Peer-to-Peer desktop grids. Futur. Gener. Comput. Syst. 36, 209–220 (2014)CrossRefGoogle Scholar
  30. 30.
    Top 500 Supercomputing Sited, [Online], Available:
  31. 31.
    Wang, X., Wang, Y., Cui, Y.: A new multi-objective bi-level programming model for energy and locality aware multi-job scheduling in cloud computing. Futur. Gener. Comput. Syst. 36, 91–101 (2014)CrossRefGoogle Scholar
  32. 32.
    Wang, L., et al.: Energy-aware parallel task scheduling in a cluster. Futur. Gener. Comput. Syst. 29(7), 1661–1670 (2013)CrossRefGoogle Scholar
  33. 33.
    Zhang, Y.-w., Guo, R.-f.: Power-aware scheduling algorithms for sporadic tasks in real-time systems. J. Syst. Softw. 86(10), 2611–2619 (2013)CrossRefGoogle Scholar
  34. 34.
    Zhang, Y., Lu, C., Zhang, H., Han, J.: Active vibration isolation system integrated optimization based on multi-objective genetic algorithm. In: IEEE 2nd International Conference on Computing, Control and Industrial Engineering (CCIE), pp. 258–261 (2011)Google Scholar

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

Personalised recommendations