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Task scheduling using fuzzy logic with best-fit-decreasing for cloud computing environment

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Abstract

An efficient task scheduling is mandatory in cloud computing for providing virtual resources used to carry out the tasks. An effective allocation of VM with the presence of diverse resource requirements, inaccurate information and uncertainties existing in the system is difficult. In this research, an effective task scheduling is done by using the fuzzy logic (FL) with best-fit-decreasing (BFD) in a cloud computing environment. The developed FL–BFD is optimized using resource usage, power, cost and time. Accordingly, the FL–BFD reallocates virtual machine (VM) in the cloud, based on the user demands. Therefore, the adaptability of FL is leveraged to handle uncertainties and imprecise information, which is helpful for an appropriate allocation of VM using BFD according to user requirements. The developed FL–BFD is analyzed using makespan, execution time, degree of imbalance, energy consumption and service level agreements (SLA) violations. The existing approaches named minimum completion time (MCT), particle swarm optimization (PSO), improved wild horse optimization with levy flight algorithm for task scheduling in cloud computing (IWHOLF-TSC), inverted ant colony optimisation (IACO), fuzzy system and modified particle swarm optimization (FMPSO), and task-scheduling using whale optimization (TSWO) are used for comparison. The makespan of FL–BFD with 1000 tasks is 9.2 ms, which is higher when compared to the IWHOLF-TSC and MCT-PSO.

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References

  1. Siddesha, K., Jayaramaiah, G.V., Singh, C.: A novel deep reinforcement learning scheme for task scheduling in cloud computing. Cluster Comput. 25(6), 4171–4188 (2022). https://doi.org/10.1007/s10586-022-03630-2

    Article  Google Scholar 

  2. Khan, M.S.A., Santhosh, R.: Task scheduling in cloud computing using hybrid optimization algorithm. Soft. Comput.Comput. 26(23), 13069–13079 (2022). https://doi.org/10.1007/s00500-021-06488-5

    Article  Google Scholar 

  3. Mangalampalli, S., Swain, S.K., Mangalampalli, V.K.: Multi objective task scheduling in cloud computing using cat swarm optimization algorithm. Arab. J. Sci. Eng. 47(2), 1821–1830 (2022). https://doi.org/10.1007/s13369-021-06076-7

    Article  Google Scholar 

  4. Imene, L., Sihem, S., Okba, K., Mohamed, B.: A third generation genetic algorithm NSGAIII for task scheduling in cloud computing. J. King Saud Univ. Comput. Inf. Sci. 34(9), 7515–7529 (2022). https://doi.org/10.1016/j.jksuci.2022.03.017

    Article  Google Scholar 

  5. Sharma, M., Kumar, M., Samriya, J.K.: An optimistic approach for task scheduling in cloud computing. Int. J. Inf. Technol. 14(6), 2951–2961 (2022). https://doi.org/10.1007/s41870-022-01045-1

    Article  Google Scholar 

  6. Abualigah, L., Alkhrabsheh, M.: Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing. J. Supercomput.Supercomput. 78(1), 740–765 (2022). https://doi.org/10.1007/s11227-021-03915-0

    Article  Google Scholar 

  7. Praveen, S.P., Ghasempoor, H., Shahabi, N., Izanloo, F.: A hybrid gravitational emulation local search-based algorithm for task scheduling in cloud computing. Math. Probl. Eng.Probl. Eng. 2023, 6516482 (2023). https://doi.org/10.1155/2023/6516482

    Article  Google Scholar 

  8. Sharma, N., Sonal, Garg, P.: Ant colony based optimization model for QoS-based task scheduling in cloud computing environment. Meas. Sens. 24, 100531 (2022). https://doi.org/10.1016/j.measen.2022.100531

    Article  Google Scholar 

  9. Panda, S.K., Nanda, S.S., Bhoi, S.K.: A pair-based task scheduling algorithm for cloud computing environment. J. King Saud Univ. Comput. Inf. Sci. 34(1), 1434–1445 (2022). https://doi.org/10.1016/j.jksuci.2018.10.001

    Article  Google Scholar 

  10. Nayak, S.C., Parida, S., Tripathy, C., Pattnaik, P.K.: An enhanced deadline constraint based task scheduling mechanism for cloud environment. J. King Saud Univ. Comput. Inf. Sci. 34(2), 282–294 (2022). https://doi.org/10.1016/j.jksuci.2018.10.009

    Article  Google Scholar 

  11. Kang, K., Ding, D., Xie, H., Yin, Q., Zeng, J.: Adaptive DRL-based task scheduling for energy-efficient cloud computing. IEEE Trans. Netw. Serv. Manag.Netw. Serv. Manag. 19(4), 4948–4961 (2022). https://doi.org/10.1109/TNSM.2021.3137926

    Article  Google Scholar 

  12. Gupta, P., Rawat, P.S., Saini, D.K., Vidyarthi, A., Alharbi, M.: Neural network inspired differential evolution based task scheduling for cloud infrastructure. Alex. Eng. J. 73, 217–230 (2023). https://doi.org/10.1016/j.aej.2023.04.032

    Article  Google Scholar 

  13. Gupta, S., Iyer, S., Agarwal, G., Manoharan, P., Algarni, A.D., Aldehim, G., Raahemifar, K.: Efficient prioritization and processor selection schemes for HEFT algorithm: a makespan optimizer for task scheduling in cloud environment. Electronics 11(16), 2557 (2022). https://doi.org/10.3390/electronics11162557

    Article  Google Scholar 

  14. Mahmoud, H., Thabet, M., Khafagy, M.H., Omara, F.A.: Multiobjective task scheduling in cloud environment using decision tree algorithm. IEEE Access 10, 36140–36151 (2022). https://doi.org/10.1109/ACCESS.2022.3163273

    Article  Google Scholar 

  15. Pirozmand, P., Javadpour, A., Nazarian, H., Pinto, P., Mirkamali, S., Ja’fari, F.: GSAGA: a hybrid algorithm for task scheduling in cloud infrastructure. J. Supercomput.Supercomput. 78(15), 17423–17449 (2022). https://doi.org/10.1007/s11227-022-04539-8

    Article  Google Scholar 

  16. Ghafari, R., Mansouri, N.: Improved Harris hawks optimizer with chaotic maps and opposition-based learning for task scheduling in cloud environment. Cluster Comput. (2023). https://doi.org/10.1007/s10586-023-04021-x

    Article  Google Scholar 

  17. Malathi, K., Priyadarsini, K.: Hybrid lion–GA optimization algorithm-based task scheduling approach in cloud computing. Appl. Nanosci.Nanosci. 13(3), 2601–2610 (2023). https://doi.org/10.1007/s13204-021-02336-y

    Article  Google Scholar 

  18. Lipsa, S., Dash, R.K., Ivković, N., Cengiz, K.: Task scheduling in cloud computing: a priority-based heuristic approach. IEEE Access 11, 27111–27126 (2023). https://doi.org/10.1109/ACCESS.2023.3255781

    Article  Google Scholar 

  19. Saroit, I.A., Tarek, D.: LBCC-Hung: a load balancing protocol for cloud computing based on Hungarian method. Egypt. Inf. J. 24(3), 100387 (2023). https://doi.org/10.1016/j.eij.2023.100387

    Article  Google Scholar 

  20. Emami, H.: Cloud task scheduling using enhanced sunflower optimization algorithm. ICT Express 8(1), 97–100 (2022). https://doi.org/10.1016/j.icte.2021.08.001

    Article  Google Scholar 

  21. Kruekaew, B., Kimpan, W.: Multi-objective task scheduling optimization for load balancing in cloud computing environment using hybrid artificial bee colony algorithm with reinforcement learning. IEEE Access 10, 17803–17818 (2022). https://doi.org/10.1109/ACCESS.2022.3149955

    Article  Google Scholar 

  22. Alsaidy, S.A., Abbood, A.D., Sahib, M.A.: Heuristic initialization of PSO task scheduling algorithm in cloud computing. J. King Saud Univ. Comput. Inf. Sci. 34(6A), 2370–2382 (2022). https://doi.org/10.1016/j.jksuci.2020.11.002

    Article  Google Scholar 

  23. Saravanan, G., Neelakandan, S., Ezhumalai, P., Maurya, S.: Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing. J. Cloud Comput. 12, 24 (2023). https://doi.org/10.1186/s13677-023-00401-1

    Article  Google Scholar 

  24. Azad, P., Navimipour, N.J., Hosseinzadeh, M.: A fuzzy-based method for task scheduling in the cloud environments using inverted ant colony optimisation algorithm. Int. J. Bio-Inspired Comput. 14(2), 125–137 (2019). https://doi.org/10.1504/IJBIC.2019.101638

    Article  Google Scholar 

  25. Mansouri, N., Zade, B.M.H., Javidi, M.M.: Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory. Comput. Ind. Eng.. Ind. Eng. 130, 597–633 (2019). https://doi.org/10.1016/j.cie.2019.03.006

    Article  Google Scholar 

  26. Mangalampalli, S., Swain, S.K., Karri, G.R., Mishra, S.: SLA aware task-scheduling algorithm in cloud computing using whale optimization algorithm. Sci. Program. 2023, 8830895 (2023). https://doi.org/10.1155/2023/8830895

    Article  Google Scholar 

  27. Fu, X., Sun, Y., Wang, H., Li, H.: Task scheduling of cloud computing based on hybrid particle swarm algorithm and genetic algorithm. Clust. Comput.. Comput. 26(5), 2479–2488 (2023)

    Article  Google Scholar 

  28. Zade, B.M.H., Mansouri, N.: Improved red fox optimizer with fuzzy theory and game theory for task scheduling in cloud environment. J. Comput. Sci.Comput. Sci. 63, 101805 (2022)

    Google Scholar 

  29. Manikandan, N., Gobalakrishnan, N., Pradeep, K.: Bee optimization based random double adaptive whale optimization model for task scheduling in cloud computing environment. Comput. Commun.. Commun. 187, 35–44 (2022)

    Article  Google Scholar 

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Nitin Thapliyal: conceptualization; investigation; methodology; visualization; formal analysis; writing—original draft. Priti Dimri: data curation; resources; validation; supervision; writing—review and editing; project administration. All authors have read and approved the final manuscript.

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Correspondence to Nitin Thapliyal.

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Thapliyal, N., Dimri, P. Task scheduling using fuzzy logic with best-fit-decreasing for cloud computing environment. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04378-7

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