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Dynamic heterogeneous shortest job first (DHSJF): a task scheduling approach for heterogeneous cloud computing systems

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Abstract

Data and computational centres consume a large amount of energy and limited by power density and computational capacity. As compared with the traditional distributed system and homogeneous system, the heterogeneous system can provide improved performance and dynamic provisioning. Dynamic provisioning can reduce energy consumption and map the dynamic requests with heterogeneous resources. The problem of resource utilization in heterogeneous computing system has been studied with variations. Scheduling of independent, non-communicating, variable length tasks in the concern of CPU utilization, low energy consumption, and makespan using dynamic heterogeneous shortest job first (DHSJF) model is discussed in this paper. Tasks are scheduled in such a manner to minimize the actual CPU time and overall system execution time or makespan. During execution, the load is balanced dynamically. Dynamic heterogeneity achieves reduced makespan that increases resource utilization. Some existing methods are not designed for fully heterogeneous systems. Our proposed method considers both dynamic heterogeneities of workload and dynamic heterogeneity of resources. Our proposed algorithm provides the better results than existing algorithm. The proposed algorithm has been simulated on CloudSim.

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References

  1. Gupta A, Krishnaswamy R, Pruhs K (2010) Scalably scheduling power-heterogeneous processors. In: International Colloquium on Automata, Languages, and Programming, Springer, Berlin, pp 312–323

    Chapter  Google Scholar 

  2. Bower FA, Sorin DJ, Cox LP (2008) The impact of dynamically heterogeneous multicore processors on thread scheduling. IEEE Micro 28(3):17–25

    Article  Google Scholar 

  3. Djebbar EI, Belalem G (2016) Tasks scheduling and resource allocation for high data management in scientific cloud computing environment. In: International conference on mobile, Secure and programmable networking. Springer, Cham, pp 16–27

    Chapter  Google Scholar 

  4. Khare S, Dubey S (2015) An efficient load balancing in public cloud using priority based SJF scheduling. Int J Sci Eng Res 6(4):1834–1838

    Google Scholar 

  5. Devi DC, Uthariaraj VR (2016) Load balancing in cloud computing environment using improved weighted round robin algorithm for nonpreemptive dependent tasks. Sci World J 2016. https://doi.org/10.1155/2016/3896065

    Article  Google Scholar 

  6. Cargo S, Dunn K, Eads P, Hochstein L, Kang DI, Kang M, Modium D, Singh K, Suh J, Walters JP (2011) Heterogeneous cloud computing. In: IEEE international conference on cluster computing. IEEE, pp 378–385

  7. Weng L, Liu C, Gaudiot JL (2013) Scheduling optimization in multicore multithreaded microprocessors through dynamic modeling. In: Proceedings of the ACM international conference on computing frontiers. ACM, pp 1–5

  8. Shreya S, Kaur A (2015) Load balancing in cloud computing using shortest job first and round robin approach. Int J Sci Res 9(4):1577–1580

    Google Scholar 

  9. Nager SK, Gill NS (2016) An improved shortest job first scheduling algorithm to decrease starvation in cloud computing. IJCSMC 5(8):155–161

    Google Scholar 

  10. Alworafi MA, Dhari A, Al-Hashmi AA, Darem AB (2016) An improved SJF scheduling algorithm in cloud computing environment. In: Electrical, electronics, communication, computer and optimization techniques (ICEECCOT), international conference. IEEE, pp 208–212

  11. Bansal N, Pruhs KR (2010) Server scheduling to balance priorities, fairness, and average quality of service. SIAM J Comput 39(7):3311–3335

    Article  MathSciNet  Google Scholar 

  12. Ren H, Lan Y, Yin C (2012) The load balancing algorithm in cloud computing environment. In: Computer science and network technology (ICCSNT), 2nd international conference. IEEE, pp 925–928

  13. Gupta A, Im S, Krishnaswamy R, Moseley B, Pruhs K (2012) Scheduling heterogeneous processors isn’t as easy as you think. In: Proceedings of the twenty-third annual ACM-SIAM symposium on discrete algorithms. Society for Industrial and Applied Mathematics, pp 1242–1253

  14. Zhang Q, Zhani MF, Boutaba R, Hellerstein JL (2013) harmony: dynamic heterogeneity-aware resource provisioning in the cloud. In: Distributed computing systems (ICDCS), IEEE 33rd international conference. IEEE, pp 510–519

  15. Zhang Q, Zhani MF, Boutaba R, Hellerstein JL (2014) Dynamic heterogeneity-aware resource provisioning in the cloud. IEEE Trans Cloud Comput 2(1):14–28

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Kanya Gurukul Campus, Dehradun, UK, India

    Sonam Seth

  2. Department of Computer Science, Kanya Gurukul Campus, Dehradun, UK, India

    Nipur Singh

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  1. Sonam Seth
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  2. Nipur Singh
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Correspondence to Sonam Seth.

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Seth, S., Singh, N. Dynamic heterogeneous shortest job first (DHSJF): a task scheduling approach for heterogeneous cloud computing systems. Int. j. inf. tecnol. 11, 653–657 (2019). https://doi.org/10.1007/s41870-018-0156-6

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  • DOI: https://doi.org/10.1007/s41870-018-0156-6

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