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Scientific Workflow Makespan Minimization in Edge Multiple Service Providers Environment

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Abstract

The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.

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At this stage, we can not provide the code because we are extending our project.

References

  1. Farid, M., Latip, R., Hussin, M., & Abdul Hamid, N. A. W. (2020). A survey on QoS requirements based on particle swarm optimization scheduling techniques for workflow scheduling in cloud computing. Symmetry, 12(4), 551.

    Article  Google Scholar 

  2. Sabahat, S., Bukhari, H., & Xia, Y. (2019). A novel completion-time-minimization scheduling approach of scientific workflows over heterogeneous cloud computing systems. International Journal of Web Services Research, 16(4), 1–20.

    Article  Google Scholar 

  3. Banerjee, S., Adhikari, M., Kar, S., & Biswas, U. (2015). Development and analysis of a new cloudlet allocation strategy for QoS improvement in cloud. Arabian Journal for Science and Engineering, 40(5), 1409–1425.

    Article  MathSciNet  Google Scholar 

  4. Garg, S. K., Toosi, A. N., Gopalaiyengar, S. K., & Buyya, R. (2014). SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. Journal of Network and Computer Applications, 45, 108–120.

    Article  Google Scholar 

  5. Wang, P., Lei, Y., Agbedanu, P. R., & Zhang, Z. (2020). Makespan-driven workflow scheduling in clouds using immune-based PSO algorithm. IEEE Access, 8, 29281–29290.

    Article  Google Scholar 

  6. Zhang, R., & Shi, W. (2021). Research on workflow task scheduling strategy in edge computer environment. Journal of Physics: Conference Series, 1744(3), 032215.

    Google Scholar 

  7. Konjaang, J. K., & Xu, L. (2021). Multi-objective workflow optimization strategy (MOWOS) for cloud computing. Journal of Cloud Computing, 10(1), 1–19.

    Article  Google Scholar 

  8. Li, Z., Ge, J., Hu, H., Song, W., Hu, H., & Luo, B. (2015). Cost and energy aware scheduling algorithm for scientific workflows with deadline constraint in clouds. IEEE Transactions on Services Computing, 11(4), 713–726.

    Article  Google Scholar 

  9. Chawla, Y., & Bhonsle, M. (2012). A study on scheduling methods in cloud computing. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 1(3), 12–17.

    Google Scholar 

  10. Lin, Y., & Shen, H. (2017). CloudFog: leveraging fog to extend cloud gaming for thin-client MMOG with high quality of service. IEEE Transactions on Parallel and Distributed Systems, 28(2), 431–445.

    Article  MathSciNet  Google Scholar 

  11. Gu, L., Zeng, D., Guo, S., Barnawi, A., & Xiang, Y. (2015). Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Transactions on Emerging Topics in Computing, 5(1), 108–119.

    Article  Google Scholar 

  12. Mukherjee, A., De, D., & Roy, D. G. (2016). A power and latency aware cloudlet selection strategy for multi-cloudlet environment. IEEE Transactions on Cloud Computing, 7(1), 141–154.

    Article  Google Scholar 

  13. Deng, R., Lu, R., Lai, C., Luan, T. H., & Liang, H. (2016). Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE internet of things journal, 3(6), 1171–1181.

    Google Scholar 

  14. Jia, M., Cao, J., & Liang, W. (2017). Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Transactions on Cloud Computing, 5(4), 725–737. https://doi.org/10.1109/TCC.2015.2449834

    Article  Google Scholar 

  15. hang, H., Xiao, Y., BuNiyato, S. D., Yu, F. R., & Han, Z. (2017). Computing resource allocation in three-tier IoT fog networks: A joint optimization approach combining Stackelberg game and matching. IEEE Internet of Things Journal, 4(5), 1204–1215.

    Article  Google Scholar 

  16. Paik, I., Ishizuka, Y., Do, Q.-M., Chen, W. (2018). On-line cost-aware workflow allocation in heterogeneous computing environments," In 2018 IEEE 12th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), pp. 209–216 IEEE

  17. Derhamy, H., Andersson, M., Eliasson, J., & Delsing, J. (2018). Workflow management for edge driven manufacturing systems. IEEE Industrial Cyber-Physical Systems (ICPS), 2018, 774–779.

    Article  Google Scholar 

  18. Al Ridhawi, I., Kotb, Y., & Al Ridhawi, Y. (2017). Workflow-net based service composition using mobile edge nodes. IEEE Access, 5, 23719–23735.

    Article  Google Scholar 

  19. Wu, X., Deng, M., Zhang, R., Zeng, B., & Zhou, S. (2013). A task scheduling algorithm based on QoS-driven in cloud computing. Procedia Computer Science, 17, 1162–1169.

    Article  Google Scholar 

  20. Ramakrishnan, S., Reutiman, R. (2013). A. Chandra, J. Weissman, "accelerating distributed workflows with edge resources," In 2013 IEEE international symposium on parallel distributed processing, Workshops Phd Forum, pp. 2129-2138 IEEE.

  21. Al-Khanak, E. N. (2021). A heuristics-based cost model for scientific workflow scheduling in cloud. CMC Computer Materials Continua, 67, 3265–3282.

    Article  Google Scholar 

  22. Rausch, T., Rashed, A., & Dustdar, S. (2021). Optimized container scheduling for data-intensive serverless edge computing. Future Generation Computer Systems, 114, 259–271. https://doi.org/10.1016/j.future.2020.07.017

    Article  Google Scholar 

  23. Meena, J., Kumar, M., & Vardhan, M. J. I. A. (2016). Cost effective genetic algorithm for workflow scheduling in cloud under deadline constraint. IEEE Access, 4, 5065–5082.

    Article  Google Scholar 

  24. Zhu, Z., Zhang, G., Li, M., & Liu, X. (2015). Evolutionary multi-objective workflow scheduling in cloud. IEEE Transactions on parallel and distributed Systems, 27(5), 1344–1357.

    Article  Google Scholar 

  25. Chen, Z.-G., Du, K.-J., Zhan, Z.-H., Zhang, J. (2015). Deadline constrained cloud computing resources scheduling for cost optimization based on dynamic objective genetic algorithm. In 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 708–714: IEEE.

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Authors and Affiliations

  1. College of Computer Science, Neijiang Normal University, Neijiang, 641100, People’s Republic of China

    S. Sabahat H. Bukhari

  2. Ecole Mathématiques, Informatique, Télécommunications de Toulouse, Université de Toulouse, Toulouse, France

    Muhammad Usman Younus

  3. Department of Computer Science & IT, University of Jhang, Jhang, Pakistan

    Muhammad Usman Younus

  4. College of Physics and Electronic Information Engineering, Neijiang Normal University, Neijiang, 641100, People’s Republic of China

    Zain ul Abidin Jaffri

  5. Department of Engineering Technology, The University of Faisalabad, Faisalabad, 38000, Pakistan

    Muhammad Arshad Shehzad Hassan

  6. Department of Electronic Engineering, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan

    Muhammad Rizwan Anjum

  7. Department of Computer Systems Engineering, Mehran University of Engineering & Technology (MUET), Jamshoro, Pakistan

    Sanam Narejo

Authors
  1. S. Sabahat H. Bukhari
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  2. Muhammad Usman Younus
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  3. Zain ul Abidin Jaffri
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  4. Muhammad Arshad Shehzad Hassan
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  5. Muhammad Rizwan Anjum
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  6. Sanam Narejo
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All authors contributed equally.

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Correspondence to Muhammad Usman Younus.

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Bukhari, S.S.H., Younus, M.U., Jaffri, Z.u.A. et al. Scientific Workflow Makespan Minimization in Edge Multiple Service Providers Environment. Wireless Pers Commun 125, 3187–3203 (2022). https://doi.org/10.1007/s11277-022-09704-z

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  • DOI: https://doi.org/10.1007/s11277-022-09704-z

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