Hybrid Job Scheduling Algorithm for Cloud Computing Environment

  • Saeed Javanmardi
  • Mohammad Shojafar
  • Danilo Amendola
  • Nicola Cordeschi
  • Hongbo Liu
  • Ajith Abraham
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 303)


In this paper with the aid of genetic algorithm and fuzzy theory, we present a hybrid job scheduling approach, which considers the load balancing of the system and reduces total execution time and execution cost. We try to modify the standard Genetic algorithm and to reduce the iteration of creating population with the aid of fuzzy theory. The main goal of this research is to assign the jobs to the resources with considering the VM MIPS and length of jobs. The new algorithm assigns the jobs to the resources with considering the job length and resources capacities. We evaluate the performance of our approach with some famous cloud scheduling models. The results of the experiments show the efficiency of the proposed approach in term of execution time, execution cost and average Degree of Imbalance (DI).


Cloud computing Scheduling Genetic algorithm fuzzy theory Makespan 


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  1. 1.
    Mezmaz, M., Melab, N., Kessaci, Y., Lee, Y.C., Talbi, E.G., Zomaya, A.Y., Tuyttens, D.: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. Journal of Parallel and Distributed Computing 71(11), 1497–1508 (2011)CrossRefGoogle Scholar
  2. 2.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Communications of the ACM 53(4), 50–58 (2010)CrossRefGoogle Scholar
  3. 3.
    Dikaiakos, M.D., Katsaros, D., Mehra, P., Pallis, G., Vakali, A.: Cloud computing: distributed internet computing for IT and scientific research. IEEE Internet Computing 13(5), 10–13 (2009)CrossRefGoogle Scholar
  4. 4.
    Maguluri, S.T., Srikant, R., Lei, Y.: Stochastic models of load balancing and scheduling in cloud computing clusters. In: IEEE Proceedings (INFOCOM), pp. 702–710 (2012)Google Scholar
  5. 5.
    Li, Q., Yike, G.: Optimization of Resource Scheduling in Cloud Computing. In: IEEE SYNASC, pp. 315–320 (2010)Google Scholar
  6. 6.
    Pooranian, Z., Harounabadi, A., Shojafar, M., Hedayat, N.: New hybrid algorithm for task scheduling in grid computing to decrease missed task. World Academy of Science, Engineering and Technology 55, 5–9 (2011)Google Scholar
  7. 7.
    Zhong, H., Kun, T., Xuejie, Z.: An approach to optimized resource scheduling algorithm for open-source cloud systems. In: IEEE ChinaGrid Conference (ChinaGrid), pp. 124–129 (2010)Google Scholar
  8. 8.
    Cordeschi, N., Shojafar, M., Baccarelli, E.: Energy-saving self-configuring networked data centers. Computer Networks 57(17), 3479–3491 (2013)CrossRefGoogle Scholar
  9. 9.
    Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Machine Learning 3(2-3), 95–99 (1988)CrossRefGoogle Scholar
  10. 10.
    Pooranian, Z., Shojafar, M., Abawajy, J.H., Abraham, A.: An efficient meta-heuristic algo-rithm for grid computing. Journal of Combinatorial Optimization, JOCO (2013), doi:10.1007/s10878-013-9644-6Google Scholar
  11. 11.
    Vas, P.: Artificial-intelligence-based electrical machines and drives: application of fuzzy, neural, fuzzy-neural, and genetic-algorithm-based techniques, p. 45. Oxford University Press (1999)Google Scholar
  12. 12.
    Javanmardi, S., Shojafar, M., Shariatmadari, S., Ahrabi, S.S.: FRTRUST: a Fuzzy Reputation Based Model for Trust Management in Semantic P2P Grids. InderScience, International Journal of Grid and Utility Computing (accepted forthcoming list, 2014).Google Scholar
  13. 13.
    Zarrazvand, H., Shojafar, M.: The Use of Fuzzy Cognitive Maps in Analyzing and Implementation of ITIL Processes. International Journal of Computer Science Issues (IJCSI) 9(3) (2012)Google Scholar
  14. 14.
    Mamdani, E.H.: Application of fuzzy algorithms for control of simple dynamic plant. In: Proceedings of the Institution of Electrical Engineers, vol. 121(12). IET Digital Library (1974)Google Scholar
  15. 15.
    Randles, M., Lamb, D., Taleb-Bendiab, A.: A comparative study into distributed load balancing algorithms for cloud computing. In: IEEE Advanced Information Networking and Applications Workshops (WAINA), pp. 551–556 (2010)Google Scholar
  16. 16.
    Baowen, X., Yu, G., Zhenqiang, C., Leung, K.R.P.H.: Parallel genetic algorithms with schema migration. In: Computer Software and Applications Conference (COMPSAC), pp. 879–884 (2002)Google Scholar
  17. 17.
    Zhongni, Z., Wang, R., Hai, Z., Xuejie, Z.: An approach for cloud resource scheduling based on Parallel Genetic Algorithm. In: IEEE ICCRD, vol. 2, pp. 444–447 (2011)Google Scholar
  18. 18.
    Singh, R.M., Sendhil Kumar, K.S., Jaisankar, N.: Comparison of Probabilistic Optimization Algorithms for resource scheduling in Cloud Computing Environment. International Journal of Engineering and Technology (IJET) 5(2), 1419–1427 (2013)Google Scholar
  19. 19.
    Li, J., Qian, W., Cong, W., Ning, C., Kui, R., Wenjing, L.: Fuzzy Keyword Search over Encrypted Data in Cloud Computing. In: IEEE INFOCOM, pp. 1–5 (2010)Google Scholar
  20. 20.
    Fang, Y., Wang, F., Ge, J.: A Task Scheduling Algorithm Based on Load Balancing in Cloud Computing. In: Wang, F.L., Gong, Z., Luo, X., Lei, J. (eds.) WISM 2010. LNCS, vol. 6318, pp. 271–277. Springer, Heidelberg (2010)Google Scholar
  21. 21.
    Chen, S., Wu, J., Lu, Z.: A Cloud Computing Resource Scheduling Policy Based on Genetic Algorithm with Multiple Fitness. In: IEEE 12th International Conference on Computer and Information Technology, pp. 177–184 (2012)Google Scholar
  22. 22.
    Sawant, S.: A Genetic Algorithm Scheduling Approach for Virtual Machine Resources in a Cloud Computing Environment. Msc Thesis (2011)Google Scholar
  23. 23.
    Calheiros, R.N., et al.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience 41(1), 23–50 (2011)MathSciNetGoogle Scholar
  24. 24.
    Nishant, K., et al.: Load Balancing of Nodes in Cloud Using Ant Colony Optimization. In: IEEE UKSim, pp. 3–8 (2012)Google Scholar
  25. 25.
    Wronikowska, M.W.: Coping with the Complexity of Cognitive Decision-Making: The TOGA Meta-Theory Approach. In: Proceedings in Complexity, pp. 427–433. Springer (2013)Google Scholar
  26. 26.
    Yonggui, W., Ruilian, H.: Study on Cloud Computing Task Schedule Strategy Based on MACO Algorithm. Computer Measurement & Control (2011)Google Scholar
  27. 27.
    Abolfazli, S., Sanaei, Z., Alizadeh, M., Gani, A., Xia, F.: An experimental analysis on cloud-based mobile augmentation in mobile cloud computing. IEEE Transactions on Consumer Electronics 60(1), 146–154 (2014)CrossRefGoogle Scholar
  28. 28.
    Sanaei, Z., Abolfazli, S., Gani, A.: Hybrid Pervasive Mobile Cloud Computing: Toward Enhancing Invisibility. Information 16(11), 8145–8181 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Saeed Javanmardi
    • 1
  • Mohammad Shojafar
    • 2
  • Danilo Amendola
    • 2
  • Nicola Cordeschi
    • 2
  • Hongbo Liu
    • 3
  • Ajith Abraham
    • 4
    • 5
  1. 1.Department of Computer EngineeringIslamic Azad UniversityDezfulIran
  2. 2.Department of Information Engineering Electronics and Telecommunications (DIET)University Sapienza of RomeRomeItaly
  3. 3.School of InformationDalian Maritime UniversityDalianChina
  4. 4.IT4InnovationsVSB-Technical University of OstravaOstravaCzech Republic
  5. 5.Machine Intelligence Research Labs (MIR Labs)Scientific Network for Innovation, and Research ExcellenceAuburnUSA

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