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Vladimir O.
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doi: 10.17586/2226-1494-2023-23-2-313-322

Hybrid JAYA algorithm for workflow scheduling in cloud

S. K. Bothra, S. Singhal, H. Goyal


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Article in English

For citation:
Bothra S.K., Singhal S., Goyal H. Hybrid JAYA algorithm for workflow scheduling in cloud. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 2, pp. 313–322. doi: 10.17586/2226-1494-2023-23-2-313-322


Abstract
Workflow scheduling and resource provisioning are two of the most critical issues in cloud computing. Developing an optimal workflow scheduling strategy in the heterogeneous cloud environment is extremely difficult due to its NP-complete nature. Various optimization algorithms have been used to schedule the workflow so that users can receive Quality of Service (QoS) from cloud service providers as well as service providers can achieve maximum gain but there is no such model that can simultaneously minimize execution time and cost while balancing the load among virtual machines in a heterogeneous environment using JAYA approach. In this article, we employed the hybrid JAYA algorithm to minimize the computation cost and completion time during workflow scheduling. We considered the heterogeneous cloud computing environment and made an effort to evenly distribute the load among the virtual machines. To achieve our goals, we used the Task Duplication Heterogeneous Earliest Finish Time (HEFT-TD) and Predict Earliest Finish Time (PEFT). The makespan is greatly shortened by HEFT-TD which is based on the Optimistic Cost Table. We used a greedy technique to distribute the workload among Virtual Machines (VMs) in a heterogeneous environment. Greedy approach assigns the upcoming task to a VM which have lowest load. In addition, we also considered performance variation, termination delay, and booting time of virtual machines to achieve our objectives in our proposed model. We used Montage, LIGO, Cybershake, and Epigenomics datasets to experimentally analyze the suggested model in order to validate the concept. Our meticulous experiments show that our hybrid approach outperforms other recent algorithms in minimizing the execution cost and makespan, such as the Cost Effective Genetic Algorithm (CEGA), Cost-effective Load-balanced Genetic Algorithm (CLGA), Cost effective Hybrid Genetic Algorithm (CHGA), and Artificial Bee Colony Algorithm (ABC).

Keywords: JAYA algorithm, workflow scheduling, execution cost, makespan, load balance, cloud computing

References
  1. Yu J., Buyya R. A taxonomy of scientific workflow systems for grid computing. ACM SIGMOD Record, 2005, vol. 34, no. 3. pp. 44–49. https://doi.org/10.1145/1084805.1084814
  2. Singhal S., Grover J. Hybrid biogeography algorithm for reducing power consumption in cloud computing. Proc. of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017, pp. 121–124. https://doi.org/10.1109/ICACCI.2017.8125827
  3. Bothra S.K., Singhal S. Nature-inspired metaheuristic scheduling algorithms in cloud: A systematic review. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2021, vol. 21, no. 4, pp. 463–472. https://doi.org/10.17586/2226-1494-2021-21-4-463-472
  4. Bhatt C., Singhal S. Anatomy of virtual machine placement techniques in cloud. Lecture Notes in Networks and Systems, 2022, vol. 373, pp. 609–626. https://doi.org/10.1007/978-981-16-8721-1_59
  5. Singhal S., Patel J. Load balancing scheduling algorithm for concurrent workflow. Computing and Informatics, 2018, vol. 37, no. 2, pp. 311–326. https://doi.org/10.4149/cai_2018_2_311
  6. NoorianTalouki R., Hosseini Shirvani M., Motameni H. A heuristic-based task scheduling algorithm for scientific workflows in heterogeneous cloud computing platforms. Journal of King Saud University - Computer and Information Sciences, 2022, vol. 34, no. 8, pp. 4902–4913. https://doi.org/10.1016/j.jksuci.2021.05.011
  7. Arabnejad H., Barbosa J.G. List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Transactions on Parallel and Distributed Systems,2014, vol. 25, no. 3, pp. 682–694. https://doi.org/10.1109/tpds.2013.57
  8. Shubham, Gupta R., Gajera V., Jana P.K. An effective multi-objective workflow scheduling in cloud computing: a pso based approach. Proc. of the 2016 Ninth International Conference on Contemporary Computing (IC3), 2016, pp. 1–6. https://doi.org/10.1109/ic3.2016.7880196
  9. Lal A., Krishna C.R. Critical path-based ant colony optimization for scientific workflow scheduling in cloud computing under deadline constraint. Advances in Intelligent Systems and Computing, 2018, vol. 696, pp. 447–461. https://doi.org/10.1007/978-981-10-7386-1_39
  10. Xu R., Wang Y., Cheng Y., Zhu Y., Xie Y., Sani A.S., Yuan D. Improved particle swarm optimization based workflow scheduling in cloud-fog environment. Lecture Notes in Business Information Processing, 2019, vol. 342, pp. 337–347. https://doi.org/10.1007/978-3-030-11641-5_27
  11. Meshkati J., Safi-Esfahani F. Energy-aware resource utilization based on particle swarm optimization and artificial bee colony algorithms in cloud computing. The Journal of Supercomputing, 2019, vol. 75, no. 5, pp. 2455–2496. https://doi.org/10.1007/s11227-018-2626-9
  12. Mohanty S., Patra P.K., Ray M., Mohapatra S. An approach for load balancing in cloud computing using JAYA algorithm. International Journal of Information Technology and Web Engineering, 2019, vol. 14, no. 1, pp. 27–41. https://doi.org/10.4018/IJITWE.2019010102
  13. Nayak S.C., Parida S., Tripathy C., Pattnaik P.K. Dynamic backfilling algorithm to increase resource utilization in cloud computing. International Journal of Information Technology and Web Engineering, 2019, vol. 14, no. 1, pp. 1–26. https://doi.org/10.4018/IJITWE.2019010101
  14. Masadeh R.M.T., Sharieh A.-A.A., Mahafzah B.A. Humpback whale optimization algorithm based on vocal behavior for task scheduling in cloud computing. International Journal of Advanced Science and Technology, 2019, vol. 13, no. 3, pp. 121–140.
  15. Sanaj M.S., Prathap P.J. An efficient approach to the map-reduce framework and genetic algorithm based whale optimization algorithm for task scheduling in cloud computing environment. Materials Today: Proceedings, 2021, vol. 37, pp. 3199–3208. https://doi.org/10.1016/j.matpr.2020.09.064
  16. Gu Y., Budati C. Energy-aware workflow scheduling and optimization in clouds using bat algorithm. Future Generation Computer Systems,2020, vol. 113, pp. 106–112. https://doi.org/10.1016/j.future.2020.06.031
  17. Ullah A., Nawi N.M., Khan M.H. BAT algorithm used for load balancing purpose in cloud computing: an overview. International Journal of High Performance Computing and Networking, 2020, vol. 16, no. 1, pp. 43. https://doi.org/10.1504/ijhpcn.2020.110258
  18. Aziza H., Krichen S. A hybrid genetic algorithm for scientific workflow scheduling in cloud environment. Neural Computing and Applications, 2020, vol. 32, no. 18, pp. 15263–15278. https://doi.org/10.1007/s00521-020-04878-8
  19. Sardaraz M., Tahir M. A parallel multi-objective genetic algorithm for scheduling scientific workflows in cloud computing. International Journal of Distributed Sensor Networks, 2020, vol. 16, no. 8. https://doi.org/10.1177/1550147720949142
  20. Gupta S., Agarwal I., Singh R.S. Workflow scheduling using Jaya algorithm in cloud. Concurrency and Computation: Practice and Experience, 2019, vol. 31, no. 17, pp. e5251. https://doi.org/10.1002/cpe.5251
  21. Rao R.V. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 2016, vol. 7, no. 1, pp. 19–34. https://doi.org/10.5267/j.ijiec.2015.8.004
  22. Bothra S.K., Singhal S., Goyal H. Deadline-constrained cost-effective load-balanced improved genetic algorithm for workflow scheduling. International Journal of Information Technology and Web Engineering, 2021, vol. 16, no. 4, pp. 1–34. https://doi.org/10.4018/IJITWE.2021100101
  23. Bothra S.K., Singhal S., Goyal H. Cost effective hybrid genetic algorithm for workflow scheduling in cloud. System Research and Information Technologies, 2022, vol. 3, pp. 121–138. https://doi.org/10.20535/srit.2308-8893.2022.3.08
  24. Golosov P.E., Gostev I.M. Cloud computing simulation model with a sporadic mechanism of parallel task solving control. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2022, vol. 22, no. 2, pp. 269–278. (in Russian). https://doi.org/10.17586/2226-1494-2022-22-2-269-278
  25. Arkhipov I.V. Genetic algorithm application for multi-criteria scheduling problem. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2015, vol. 15, no. 3, pp. 525–531. (in Russian). https://doi.org/10.17586/2226-1494-2015-15-3-525-531
  26. Becker M., Gatchin Y.A., Karmanovskiy N.S., Terentiev A.O., Fyodorov D.Y. Information security in cloud computing: problems and prospects. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2011, vol. 11, no. 1, pp. 97–102. (in Russian)


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