Efficient task allocation approach using genetic algorithm for cloud environment


As the number of cloud applications is rising exponentially, efficient allocation of these tasks among multiple computing machines ensuring the quality of service and better profit to the cloud service providers is a challenge. Effective task allocation approach needs to be developed considering a number of objectives while making allocation decisions, such as less energy consumption and quick response, in order to make the best resource allocation satisfying the cloud user requirements and improving the overall performance of the cloud computing environment. Hence, in this paper, Genetic Algorithm based efficient task allocation approach has been proposed for achieving the reduced task completion time by making wise allocation decisions. This proposed algorithm has been simulated using cloudsim toolkit and the performance is evaluated by comparing with greedy and simple allocation methods on a set of parameters like makespan and throughput for task scheduling. The evaluation results have shown the better throughput with the proposed approach.

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  1. 1.

    Ge, J., He, Q., Fang, Y.: Cloud computing task scheduling strategy based on improved differential evolution algorithm. In: International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation. AIP, Melville (2017)

  2. 2.

    Aggarwal, M., Kumar, N., Kaushik, A.: Review of research issues in cloud computing. Int. J. Appl. Eng. Res. 9(21), 9479–9488 (2014)

    Google Scholar 

  3. 3.

    Prasad, R.B., Eunm, C., Lumb, I.: A taxonomy and survey of cloud computing systems. NCM 2009: 5th International Joint Conference on INC, IMS, and IDC, pp. 44–51 (2009)

  4. 4.

    Wickremasinghe B., Calheiros, R. N., Buyya, R.: Cloud analyst: a cloudsim-based visual modeller for analysing cloud computing environments and applications. In: Advanced Information Networking and Applications, pp. 446–452 (2010)

  5. 5.

    Manvi, S.S., Shyam, G.K.: Resource management for Infrastructure as a service (IaaS) in cloud computing: a survey. J. Netw. Comput. Appl. 41, 424–440 (2014)

    Article  Google Scholar 

  6. 6.

    Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Khan, S.U.: A survey and taxonomy on energy-efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016)

    MathSciNet  Article  Google Scholar 

  7. 7.

    Beloglazov, A., Buyya, R., Lee, Y.C., Zomaya, A.: A taxonomy and survey of energy-efficient data centers and cloud computing systems. Adv. Comput. 82(2), 47–111 (2011)

    Article  Google Scholar 

  8. 8.

    Mishra, R.K., Bhukya, S.N.: Service broker algorithm for cloud-analyst. Int. J. Comput. Sci. Inf. Technol. 5(3), 3957–3962 (2014)

    Google Scholar 

  9. 9.

    Ge, Y., Wei, G.: GA-based task scheduler for the cloud computing systems. Web Inf. Syst. Min. 2, 181–186 (2010)

    Google Scholar 

  10. 10.

    Xu, M., Tian, W., Buyya, R.: A survey on load balancing algorithms for VM placement in cloud computing. Concur. Comput. 29(12), e4123 (2017)

    Article  Google Scholar 

  11. 11.

    Radhakrishnan, A., Kavitha, V.: Energy conservation in cloud data centres by minimizing virtual machines migration through artificial neural network. Computing 98(11), 1185–1202 (2016)

    MathSciNet  Article  Google Scholar 

  12. 12.

    Balagoni, Y., Rao, R.R.: Locality-load-prediction aware multi-objective task scheduling in the heterogeneous cloud environment. Indian J. Sci. Technol. 10(9), 1–9 (2017)

    Article  Google Scholar 

  13. 13.

    Yang, L., Cao, J., Liang, G., Han, X.: Cost-aware service placement and load dispatching in mobile cloud systems. IEEE Trans. Comput. 65(5), 1440–1452 (2016)

    MathSciNet  MATH  Article  Google Scholar 

  14. 14.

    Zhan, Z.H., Liu, X.F., Gong, Y.J., Zhang, J., Chung, H.S.H., Li, Y.: Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput. 47(4), 63 (2015)

    Google Scholar 

  15. 15.

    Piraghaj, S.F., Calheiros, R.N., Chan, J., Dastjerdi, A.V., Buyya, R.: Virtual machine customization and task mapping architecture for efficient allocation of cloud data centre resources. Comput. J. 59(2), 208–224 (2016)

    Article  Google Scholar 

  16. 16.

    Xu, Q., Xu, Z., Wang, T.: A data-placement strategy based on genetic algorithm in cloud computing. Int. J. Intell. Sci. 5(03), 145 (2015)

    Article  Google Scholar 

  17. 17.

    Lin, C., Lu, S.: Scheduling scientific workflows elastically for cloud computing. In: Proceedings of the IEEE 4th International Conference on Cloud Computing, Washington, DC, USA (2011)

  18. 18.

    Kumar, P., Verma, A.: Independent task scheduling in cloud computing by improved genetic algorithm. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2(5), 111–114 (2012)

    MathSciNet  Google Scholar 

  19. 19.

    Jang, S.H., Kim, T.Y., Kim, J.K., Lee, J.S.: The study of genetic algorithm based task scheduling for cloud computing. Int. J. Control Autom. 4(5), 157–162 (2012)

    Google Scholar 

  20. 20.

    Kaleeswaran, A., Ramasamy, V., Vivekananda, P.: Dynamic scheduling of data using genetic algorithm in cloud computing. Int. J. Adv. Eng. Technol. 5(2), 327–334 (2013)

    Google Scholar 

  21. 21.

    Mehdi, N.A., Mamat, A., Ibrahim, H., Subramaniam, H.K.: Inpatient task mapping in elastic cloud using genetic algorithm. J. Comput. Sci. 7(6), 877–883 (2011)

    Article  Google Scholar 

  22. 22.

    Gu, J., Hu, J., Zhao, T., Sun, G.: A new resource scheduling strategy based on genetic algorithm in cloud computing environment. J. Comput. 7(1), 42–52 (2012)

    Article  Google Scholar 

  23. 23.

    Kaur, S., Verma, A.: An efficient approach to genetic algorithm for task scheduling in cloud computing environment. Int. J. Inf. Technol. Comput. Sci. 10, 74–79 (2012)

    Google Scholar 

  24. 24.

    Dakshayini, M., Guruprasad, H.S.: An optimal model for priority-based service scheduling policy for cloud computing environment. Int. J. Comput. Appl. 32(9), 23–29 (2011)

    Google Scholar 

  25. 25.

    Ge, Y., Wei, G.: GA-based task scheduler for the cloud computing systems. Proc. Int. Conf. Web Inf. Syst. Min. 2, 181–186 (2010)

    Google Scholar 

  26. 26.

    Lin, B., Guo, W., Xiong, N., Chen, G., Vasilakos, A., Zhang, H.: A pre-treatment workflow scheduling approach for big data applications in multi-cloud environments. IEEE Trans. Netw. Serv. Manage. 13(1), 1–12 (2016)

    Article  Google Scholar 

  27. 27.

    Kumar, N., Aggarwal, M., Kumar, R.: A comparative analysis of scheduling algorithms affecting QoS in cloud environment. Int. J. Comput. Sci. Netw. 4(1), 142–147 (2015)

    MathSciNet  Google Scholar 

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Correspondence to P. M. Rekha.

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Rekha, P.M., Dakshayini, M. Efficient task allocation approach using genetic algorithm for cloud environment. Cluster Comput 22, 1241–1251 (2019). https://doi.org/10.1007/s10586-019-02909-1

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  • Cloud computing
  • Genetic Algorithm
  • Task scheduling
  • Cloudlets
  • Makespan
  • Minimum Finishing time