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Cluster Computing

, Volume 22, Supplement 4, pp 8953–8964 | Cite as

Designing towards an efficient job aware scheduling algorithm for IaaS cloud

  • D. Venkata Vara PrasadEmail author
  • Suresh Jaganathan
Article
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Abstract

In this paper, a new job aware scheduling algorithm for IaaS cloud is proposed. As we know IaaS cloud provides an increase in computing power, storage capacity and lowering the hardware cost and also it offers cost efficiency, scalability, elasticity and dynamic service according to requested application. Scheduling in cloud is vital as it plays an important role for ripe the benefits in-terms of cost and make-span. In scheduling, the jobs are mapped based on the characteristics and user requirements. Parameters like cost, load and resource are to be considered while scheduling. In IaaS cloud, the users pay for the resources they need for computation and the resources should be utilized efficiently for the benefit of both users and providers. Hence, scheduling should consider the jobs cost and has to fully utilize the resources to reduce the make-span, cost and increase the throughput of the system. Aggrandized job aware scheduling algorithm does load balancing in cloud with respect to the services based on resource and cost. The parameters such as make-span, number of tasks executed and cost for execution are considered to evaluate the performance of proposed algorithm.

Keywords

Cloud computing IaaS Virtual machine Scheduling Resources allocation Performance and evaluation 
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References

  1. 1.
    Supreeth, S., Biradar, S.: Scheduling virtual machines for load balancing in cloud computing platform. Int. J. Sci. Res. 2(6), 437–441 (2013)Google Scholar
  2. 2.
    Sindhu, S., Mukherjee, S.: Efficient task scheduling algorithms for cloud computing environment. In: Mantri, A., Nandi, S., Kumar, G., Kumar, S. (eds.) High Performance Architecture and Grid Computing, pp. 79–83. Springer, Berlin (2011)CrossRefGoogle Scholar
  3. 3.
    Chawla, Y., Bhonsle, M.: A study on scheduling methods in cloud computing. Int. J. Emerg. Trends Technol. Comput. Sci. 1(3), 12–17 (2012)Google Scholar
  4. 4.
    Salot, P.: A survey of various scheduling algorithm in cloud computing environment. Int. J. Res. Eng. Technol. 2, 131–135 (2013)CrossRefGoogle Scholar
  5. 5.
    Selvarani, S., Sadhasivam, G.S.: Improved cost-based algorithm for task scheduling in cloud computing. In: IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–5 (2010)Google Scholar
  6. 6.
    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.) Web Information Systems and Mining, pp. 271–277. Springer, Berlin (2010)CrossRefGoogle Scholar
  7. 7.
    Mohialdeen, I.A.: Comparative study of scheduling algorithms in cloud computing environment. J. Comput. Sci. 9(2), 252–263 (2013)CrossRefGoogle Scholar
  8. 8.
    Lee, Y.-H., Leu, S., Chang, R.-S.: Improving job scheduling algorithms in a grid environment. Future Gener. Comput. Syst. 27(8), 991–998 (2011)CrossRefGoogle Scholar
  9. 9.
    Parsa, S., Entezari-Maleki, R.: RASA: a new task scheduling algorithm in grid environment. World Appl. Sci. J. 7, 152–160 (2009)Google Scholar
  10. 10.
    El-kenawy, E.S.T., El-Desoky, A.I., Al-rahamawy, M.F.: Extended max-min scheduling using petri net and load balancing. Int. J. Soft Comput. 2(4), 198–203 (2012)Google Scholar
  11. 11.
    Elzeki, O.M., Reshad, M.Z., Elsoud, M.: A improved max-min algorithm in cloud computing. Int. J. Comput. Appl. 50(12), 22–27 (2012)Google Scholar
  12. 12.
    Ghanbari, S., Othman, M.: A priority based job scheduling algorithm in cloud computing. Proc. Eng. 50, 778–785 (2012)CrossRefGoogle Scholar
  13. 13.
    Wu, H., Tang, Z., Li, R.: A priority constrained scheduling strat-egy of multiple workflows for cloud computing. In: 14th International Conference on Advanced Communication Technology (ICACT), pp. 1086–1089. IEEE (2012)Google Scholar
  14. 14.
    Yuan, J., Jiang, X., Zhong, L., Yu, H.: Energy aware resource scheduling algorithm for data center using reinforcement learning. In: 2012 Fifth International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 435–438. IEEE (2012)Google Scholar
  15. 15.
    Nathani, A., Chaudhary, S., Somani, G.: Policy based resource allocation in IaaS cloud. Future Gener. Comput. Syst. 28(1), 94–103 (2012)CrossRefGoogle Scholar
  16. 16.
    Xavier, S., Lovesum, S.J.: A survey of various workflow scheduling algorithms in cloud environment. Int. J. Sci. Res. Publ. 3(2), 1–3 (2013)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringSSN College of EngineeringChennaiIndia

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