Skip to main content

Workflow Security Scheduling Strategy in Cloud Computing

  • Conference paper
  • First Online:
Modelling and Implementation of Complex Systems (MISC 2020)

Abstract

Cloud computing is an effective technology that delivers interesting services to customers over the Internet. It is beneficial for scientific workflow systems in view of its powerful characteristics. However, scheduling workflow system over a cloud platform has become a challenging problem. In this paper, we propose a novel workflow scheduling strategy for the hybrid cloud environments which consists of an economical distribution of tasks between the various cloud service providers, in order to provide customers with high security services. Then, we study the impact of that security services on the total cost and deadline generated by the workflow. This problem is a major gap in the workflow scheduling field and it is still insufficiently explored in the literature. Our proposed scheduling system is composed for three modules. The first module is the Pre-Scheduler, the second is the Security Enhancement Module and the third one is the Post-Scheduler. The system evaluation and the extensive simulations are performed using an extension of Cloudsim simulation tool. The results show that our strategy preserves the same cost however it affects the deadline.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    National Institute of Standards and Technology.

References

  1. Abazari, F., Analoui, M., Takabi, H., Fu, S.: MOWS: multi-objective workflow scheduling in cloud computing based on heuristic algorithm. Simul. Model. Pract. Theory 93, 119–132 (2019). https://doi.org/10.1016/j.simpat.2018.10.004. https://linkinghub.elsevier.com/retrieve/pii/S1569190X18301515

  2. Arunarani, A.R., Manjula, D., Sugumaran, V.: FFBAT: a security and cost-aware workflow scheduling approach combining firefly and bat algorithms. Concurr. Comput.: Pract. Exp. 29(24), e4295 (2017). https://doi.org/10.1002/cpe.4295

  3. Calheiros, R.N., Ranjan, R., Beloglazov, A., Rose, C.A.F.D., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)

    Article  Google Scholar 

  4. Chen, H., Zhu, X., Qiu, D., Liu, L., Du, Z.: Scheduling for workflows with security-sensitive intermediate data by selective tasks duplication in clouds. IEEE Trans. Parallel Distrib. Syst. 28(9), 2674–2688 (2017). https://doi.org/10.1109/TPDS.2017.2678507. http://ieeexplore.ieee.org/document/7872483/

  5. Davidson, S.B., Freire, J.: Provenance and scientific workflows: challenges and opportunities. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1345–1350 (2008)

    Google Scholar 

  6. Fakhfakh, F., Kacem, H.H., Kacem, A.H.: Workflow scheduling in cloud computing: a survey. In: 2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations, pp. 372–378. IEEE, Ulm, September 2014. https://doi.org/10.1109/EDOCW.2014.61. http://ieeexplore.ieee.org/document/6975385/

  7. Francis, A.O., Emmanuel, B., Zhang, D., Zheng, W., Qin, Y., Zhang, D.: Exploration of secured workflow scheduling models in cloud environment: a survey. In: 2018 Sixth International Conference on Advanced Cloud and Big Data (CBD), pp. 71–76. IEEE, Lanzhou, August 2018. https://doi.org/10.1109/CBD.2018.00022. https://ieeexplore.ieee.org/document/8530818/

  8. Hoa, C., Mehta, G., Freeman, T., Deelman, E., Keahey, K., Berriman, B., Good, J.: On the use of cloud computing for scientific workflows. In: 2008 IEEE Fourth International Conference on eScience, pp. 640–645. IEEE, Indianapolis, December 2008. https://doi.org/10.1109/eScience.2008.167. http://ieeexplore.ieee.org/document/4736878/

  9. Juve, G., Deelman, E.: Scientific workflows in the cloud. In: Cafaro, M., Aloisio, G. (eds.) Grids, Clouds and Virtualization. CCN, pp. 71–91. Springer, London (2011). https://doi.org/10.1007/978-0-85729-049-6_4

  10. Kalra, M., Singh, S.: Multi-criteria workflow scheduling on clouds under deadline and budget constraints. Concurr. Comput.: Pract. Exp. 31(17), e5193 (2019). https://doi.org/10.1002/cpe.5193

  11. Kaur, S., Bagga, P., Hans, R., Kaur, H.: Quality of service (QoS) aware workflow scheduling (WFS) in cloud computing: a systematic review. Arab. J. Sci. Eng. 44(4), 2867–2897 (2019). https://doi.org/10.1007/s13369-018-3614-3

  12. Li, Z., et al.: A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds. Future Gener. Comput. Syst. 65, 140–152 (2016). https://doi.org/10.1016/j.future.2015.12.014. https://linkinghub.elsevier.com/retrieve/pii/S0167739X15003982

  13. Makhlouf, S.A., Yagoubi, B.: Data-aware scheduling strategy for scientific workflow applications in IaaS cloud computing. Int. J. Interact. Multimed. Artif. Intell. 5(4), 75 (2019). https://doi.org/10.9781/ijimai.2018.07.002. http://www.ijimai.org/journal/node/2496

  14. Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds. Future Gener. Comput. Syst 48, 1–18 (2015)

    Article  Google Scholar 

  15. Masdari, M., ValiKardan, S., Shahi, Z., Azar, S.I.: Towards workflow scheduling in cloud computing: a comprehensive analysis. J. Netw. Comput. Appl. 66, 64–82 (2016). https://doi.org/10.1016/j.jnca.2016.01.018. https://linkinghub.elsevier.com/retrieve/pii/S108480451600045X

  16. Masdari, M., Zangakani, M.: Efficient task and workflow scheduling in inter-cloud environments: challenges and opportunities. J. Supercomput. 76(1), 499–535 (2020). https://doi.org/10.1007/s11227-019-03038-7. http://link.springer.com/10.1007/s11227-019-03038-7

  17. Mell, P., Grance, T.: The NIST Definition of Cloud Computing, p. 7 (2011)

    Google Scholar 

  18. Mishra, S., Datta-Gupta, A.: Distributions and models thereof (chap. 3). In: Mishra, S., Datta-Gupta, A. (eds.) Applied Statistical Modeling and Data Analytics, pp. 31–67. Elsevier (2018)

    Google Scholar 

  19. Shahul Hammed, S.Arunkumar, B.: Efficient workflow scheduling in cloud computing for security maintenance of sensitive data. Int. J. Commun. Syst. e4240 (2019). https://doi.org/10.1002/dac.4240

  20. Shishido, H.Y., Estrella, J.C., Toledo, C.F.M., Arantes, M.S.: Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds. Comput. Electr. Eng. 69, 378–394 (2018). https://doi.org/10.1016/j.compeleceng.2017.12.004. https://linkinghub.elsevier.com/retrieve/pii/S0045790617312259

  21. da Silva, R.F., Chen, W., Juve, G., Vahi, K., Deelman, E.: Community resources for enabling research in distributed scientific workflows. In: eScience, pp. 177–184. IEEE Computer Society (2014)

    Google Scholar 

  22. Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: issues and challenges. J. Grid Comput. 14(2), 217–264 (2016). https://doi.org/10.1007/s10723-015-9359-2

  23. Smanchat, S., Viriyapant, K.: Taxonomies of workflow scheduling problem and techniques in the cloud. Future Gener. Comput. Syst. 52, 1–12 (2015). https://doi.org/10.1016/j.future.2015.04.019. https://linkinghub.elsevier.com/retrieve/pii/S0167739X15001776

  24. Wang, P., Lei, Y., Agbedanu, P.R., Zhang, Z.: Makespan-driven workflow scheduling in clouds using immune-based PSO algorithm. IEEE Access 8, 29281–29290 (2020). https://doi.org/10.1109/ACCESS.2020.2972963. https://ieeexplore.ieee.org/document/8990144/

  25. Wen, Y., Liu, J., Dou, W., Xu, X., Cao, B., Chen, J.: Scheduling workflows with privacy protection constraints for big data applications on cloud. Future Gener. Comput. Syst. (2018). https://doi.org/10.1016/j.future.2018.03.028. https://linkinghub.elsevier.com/retrieve/pii/S0167739X17307379. S0167739X17307379

  26. Xu, X., et al.: Data placement for privacy-aware applications over big data in hybrid clouds. Secur. Commun. Netw. 2017, 1–15 (2017). https://doi.org/10.1155/2017/2376484. https://www.hindawi.com/journals/scn/2017/2376484/

  27. Zeng, L., Veeravalli, B., Li, X.: SABA: a security-aware and budget-aware workflow scheduling strategy in clouds. J. Parallel Distrib. Comput. 75, 141–151 (2015). https://doi.org/10.1016/j.jpdc.2014.09.002. https://linkinghub.elsevier.com/retrieve/pii/S0743731514001658

  28. Zhou, A.C., He, B., Ibrahim, S.: eScience and big data workflows in clouds: a taxonomy and survey (chap. 18). In: Buyya, R., Calheiros, R.N., Dastjerdi, A.V. (eds.) Big Data, pp. 431–455. Morgan Kaufmann (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Belabbas Yagoubi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hammouti, S., Yagoubi, B., Makhlouf, S.A. (2021). Workflow Security Scheduling Strategy in Cloud Computing. In: Chikhi, S., Amine, A., Chaoui, A., Saidouni, D., Kholladi, M. (eds) Modelling and Implementation of Complex Systems. MISC 2020. Lecture Notes in Networks and Systems, vol 156. Springer, Cham. https://doi.org/10.1007/978-3-030-58861-8_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58861-8_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58860-1

  • Online ISBN: 978-3-030-58861-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics