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A Hybrid Approach for Task Scheduling Using the Cuckoo and Harmony Search in Cloud Computing Environment

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Abstract

Cloud Computing is a gathering of physical and virtualized assets gave to the clients according to request and pay per uses bases via internet. Basically, the task scheduling and resource allocation two features are considered such as cost and makespan. In order to achieve better performance in task scheduling, resource allocation and task scheduling must be precisely organized and optimized jointly. Several works have been published in the literature to do the scheduling in cloud. In this paper, for enhancing the scheduling process cuckoo search (CS) and harmony search (HS) algorithm is hybrid as CHSA to improve the optimization problem. These two algorithms are effectively combined to do intelligent process scheduling. According to this, a new multi-objective function is proposed by combining cost, energy consumption, memory usage, credit and penalty. Finally, the performance of the CHSA algorithm is compared with different algorithms such as existing hybrid cuckoo gravitational search algorithm, individual CS and HS algorithm with various multi-objective parameters. By analyzing the result our proposed CHSA algorithm attain minimum cost, minimum memory usage, minimum energy consumption, minimum penalty and maximum credit compared to existing techniques.

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References

  1. Sindu, S. (2015). Task scheduling in cloud computing. International Journal of Advanced Research in Computer Engineering & Technology, 4(6), 3019–3023.

    Google Scholar 

  2. Hamad, S. A., & Omara, F. A. (2016). Genetic-based task scheduling algorithm in cloud computing environment. International Journal of Advanced Computer Science and Applications, 7(4), 550–556.

    Google Scholar 

  3. Etro, F. (2010). Introducing cloud computing. In London Conference on Cloud Computing for the Public Sector (pp. 01–20).

  4. Singh, R. M., Paul, S., & Kumar, A. (2014). Task scheduling in cloud computing: Review. International Journal of Computer Science and Information Technologies, 5(6), 7940–7944.

    Google Scholar 

  5. Bölöni, L., & Turgut, D. (2017). Value of information based scheduling of cloud computing resources. Future Generation Computer Systems, 71, 212–220.

    Article  Google Scholar 

  6. Abdullahi, M., & Ngadi, M. A. (2016). Symbiotic organism search optimization based task scheduling in cloud computing environment. Future Generation Computer Systems, 56, 640–650.

    Article  Google Scholar 

  7. Awan, M., & Shah, M. A. (2015). A survey on task scheduling algorithms in cloud computing environment. International Journal of Computer and Information Technology, 4(2), 441–448.

    Google Scholar 

  8. Ming, G., & Li, H. (2012). An improved algorithm based on max-min for cloud task scheduling. Journal of Recent Advances, 125, 217–223.

    Google Scholar 

  9. Tsai, C. W. (2014). A hyper-heuristic scheduling algorithm for cloud. IEEE Transactions on Cloud Computing, 2, 236–250.

    Article  Google Scholar 

  10. Lin, J. W., Chen, C. H., & Chang, J. M. (2013). QoS-aware data replication for data-intensive applications in cloud computing systems. IEEE Transactions on Cloud Computer, 1(1), 101–115.

    Article  Google Scholar 

  11. Sfrent, A., & Pop, F. (2015). Asymptotic scheduling for many task computing in big data platforms. Information Sciences, 319, 71–91.

    Article  MathSciNet  Google Scholar 

  12. Zhong, Z., Chen, K., Zhai, X., & Zhou, S. (2016). Virtual machine-based task scheduling algorithm in a cloud computing environment. Tsinghua Science and Technology, 21(6), 660–667.

    Article  MATH  Google Scholar 

  13. Zhu, X., Chen, C., Yang, L. T., & Xiang, Y. (2015). ANGEL: Agent-based scheduling for real-time tasks in virtualized clouds. IEEE Transactions on Computers, 64(12), 3389–3403.

    Article  MathSciNet  MATH  Google Scholar 

  14. Alkhanak, E. N., Lee, S. P., & Khan, S. U. R. (2015). Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities. Future Generation Computer Systems, 50, 3–21.

    Article  Google Scholar 

  15. Shi, T., Yang, M., Li, X., Lei, Q., & Jiang, Y. (2016). An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds. Pervasive and Mobile Computing, 27, 90–105.

    Article  Google Scholar 

  16. Jiang, H., Yi, J., Chen, S., & Zhu, X. (2016). A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly. Journal of Manufacturing Systems, 41, 239–255.

    Article  Google Scholar 

  17. Morshedlou, H., & Meybodi, M. R. (2014). Decreasing impact of sla violations: A proactive resource allocation approach for cloud computing environments. IEEE Transactions on Cloud Computing, 2(2), 156–167.

    Article  Google Scholar 

  18. Gouda, K. C., Radhika, T. V., & Akshatha, M. (2013). Priority based resource allocation model for cloud computing. International Journal of Science, Engineering and Technology Research (IJSETR), 2(1), 215–219.

    Google Scholar 

  19. Juarez, F., Ejarque, J., & Badia, R. M. (2016). Dynamic energy-aware scheduling for parallel task-based application in cloud computing. Future Generation Computer Systems, 78, 257–271.

    Article  Google Scholar 

  20. Gandomi, A. H., Yang, X.-S., & Alavi, A. H. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17–35.

    Article  Google Scholar 

  21. Wang, C.-M., & Huang, Y.-F. (2010). Self-adaptive harmony search algorithm for optimization. Expert Systems with Applications, 37(4), 2826–2837.

    Article  Google Scholar 

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Correspondence to K. Pradeep.

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Pradeep, K., Prem Jacob, T. A Hybrid Approach for Task Scheduling Using the Cuckoo and Harmony Search in Cloud Computing Environment. Wireless Pers Commun 101, 2287–2311 (2018). https://doi.org/10.1007/s11277-018-5816-0

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