Skip to main content

Advertisement

SpringerLink
Log in

Optimizing security and cost of workflow execution using task annotation and genetic-based algorithm

  • Reggular Paper
  • Published:
Computing Aims and scope Submit manuscript

Abstract

Cloud computing provides an extensible infrastructure for executing workflows that demand high processing and storage capacity. Tasks are distributed and resources selected during scheduling where choices have a significant impact on data protection. Some workflow scheduling algorithms apply security services such as authentication, integrity verification, and encryption for both sensitive and non-sensitive tasks. However, this approach requires long makespan and monetary cost for execution. In this paper, we introduce a scheduling approach that considers the user annotation of workflow tasks according to the sensitiveness. We also optimize the scheduling using a multi-population genetic algorithm for minimizing cost while meeting a deadline. Extensive experiments using three workflow applications with different ratios of sensitive tasks and data size were performed to evaluate in terms of cost, makespan, risk, and wastage. The results showed that our approach can protect sensitive tasks more appropriately while achieving a better cost compared to other approaches in the literature.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Alkhanak EN, Lee SP, Rezaei R, Parizi RM (2016) Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: a review, classifications, and open issues. J Syst Softw 113:1–26

    Article  Google Scholar 

  2. Amazon (2018) Ec2 pricing. https://aws.amazon.com/ec2/pricing. Accessed 15 Aug 2018

  3. Arunarani AR, Manjula D, Sugumaran V (2017) Ffbat: a security and cost-aware workflow scheduling approach combining firefly and bat algorithms. Concurr Comput 29(24):e4295

    Article  Google Scholar 

  4. Arunarani AR, Manjula D, Sugumaran V (2018) Ffbat: a security and cost-aware workflow scheduling approach combining firefly and bat algorithms. Concurr Comput Pract Exp 29(24):e4295

    Article  Google Scholar 

  5. Balasko A, Farkas Z, Kacsuk P (2013) Building science gateways by utilizing the generic ws-pgrade/guse workflow system. Comput Sci 14(2):307–325

  6. Barga R, Gannon D, Deelman E, Gannon DB, Shields M (2007) Scientific versus business workflows, vol 1. Springer, Berlin, pp 9–16

    Google Scholar 

  7. Chen W, Deelman E (2012) Workflowsim: a toolkit for simulating scientific workflows in distributed environments. In: Proceedings of the IEEE international conference on eScience, IEEE, pp 1–8

  8. Deelman E, Gannon D, Shields M, Taylor I (2009) Workflows and e-science: an overview of workflow system features and capabilities. Future Gener Comput Syst 25(5):528–540

    Article  Google Scholar 

  9. Deelman E, Vahi K, Juve G, Rynge M, Callaghan S, Maechling PJ, Mayani R, Chen W, da Silva RF, Livny M, Wenger K (2015) Pegasus, a workflow management system for science automation. Future Gener Comput Syst 46:17–35

    Article  Google Scholar 

  10. Eshelman LJ, Schaffer JD (1993) Real-coded genetic algorithms and interval-schemata. In: Whitley LD (ed) Foundations of genetic algorithms, foundations of genetic algorithms, vol 2. Elsevier, London, pp 187–202

    Google Scholar 

  11. Fakhfakh F, Kacem HH, Kacem AH (2014) Workflow scheduling in cloud computing: a survey. In: 2014 IEEE 18th international enterprise distributed object computing conference workshops and demonstrations, pp 372–378

  12. Franca PM, Mendes A, Moscato P (2001) A memetic algorithm for the total tardiness single machine scheduling problem. Eur J Oper Res 132:224–242

    Article  MathSciNet  Google Scholar 

  13. Hashizume K, Rosado DG, Fernández-Medina E, Fernandez EB (2013) An analysis of security issues for cloud computing. J Internet Serv Appl 4(1):1–13

    Article  Google Scholar 

  14. Jianfang C, Junjie C, Qingshan Z (2014) An optimized scheduling algorithm on cloud workflow using discrete particle swarm. Cybern Inf Technol 14(1):25–39

    MathSciNet  Google Scholar 

  15. Juve G, Chervenak A, Deelman E, Bharathi S, Mehta G, Vahi K (2013) Characterizing and profiling scientific workflows. Future Gener Comput Syst 29(3):682–692

    Article  Google Scholar 

  16. Kacsuk P, Farkas Z, Kozlovszky M, Hermann G, Balasko A, Karoczkai K, Marton I (2012) Ws-pgrade/guse generic dci gateway framework for a large variety of user communities. J Grid Comput 10(4):601–630

    Article  Google Scholar 

  17. Kumar R, Goyal R (2019) On cloud security requirements, threats, vulnerabilities and countermeasures: A survey. Comput Sci Rev 33:1–48. https://doi.org/10.1016/j.cosrev.2019.05.002

    Article  MathSciNet  Google Scholar 

  18. Li Z, Ge J, Yang H, Huang L, Hu H, Hu H, Luo B (2016a) A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds. Future Gener Comput Syst 65:140–152

    Article  Google Scholar 

  19. Li Z, Ge J, Yang H, Huang L, Hu H, Hu H, Luo B (2016b) A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds. Future Gener Comput Syst 65:140–152

    Article  Google Scholar 

  20. Liu H, Abraham A, Snášel V, McLoone S (2012) Swarm scheduling approaches for workflow applications with security constraints in distributed data-intensive computing environments. Inf Sci 192:228–243

    Article  Google Scholar 

  21. Mell PM, Grance T (2011) The nist definition of cloud computing—sp 800-145. NIST, Gaithersburg, MD, United States, Tech rep

  22. Plankensteiner K, Prodan R, Janetschek M, Fahringer T, Montagnat J, Rogers D, Harvey I, Taylor I, Balaskó Á, Kacsuk P (2013) Fine-grain interoperability of scientific workflows in distributed computing infrastructures. J Grid Comput 11(3):429–455

    Article  Google Scholar 

  23. Sharif S, Taheri J, Zomaya AY, Nepal S (2013) Mphc: preserving privacy for workflow execution in hybrid clouds. In: International conference on parallel, distributed computing technologies, pp 272–280

  24. Shishido HY, Estrella JC, Toledo CFM, Arantes MS (2017) Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds. Comput Electr Eng 69:378–394

    Article  Google Scholar 

  25. Watson P (2012) A multi-level security model for partitioning workflows over federated clouds. J Cloud Comput Adv Syst Appl 1(1):15

    Article  Google Scholar 

  26. Wu F, Wu Q, Tan Y (2015) Workflow scheduling in cloud: a survey. J Supercomput 71(9):3373–3418

    Article  Google Scholar 

  27. Xie T, Qin X (2006) Scheduling security-critical real-time applications on clusters. IEEE Trans Comput 55(7):864–879

    Article  Google Scholar 

  28. Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press, Beckington

    Google Scholar 

  29. Yang XS (2010) A new metaheuristic bat-inspired algorithm. Springer, Berlin

    Book  Google Scholar 

  30. Zeng L, Veeravalli B, Li X (2015) Saba: a security-aware and budget-aware workflow scheduling strategy in clouds. J Parallel Distrib Comput 75:141–151

    Article  Google Scholar 

  31. Zhu X, Zha Y, Jiao P, Chen H (2016) Security-aware workflow scheduling with selective task duplication in clouds. In: Proceedings of the high performance computing symposium, pp 1–8

Download references

Author information

Authors and Affiliations

  1. Department of Computing, Federal University of Technology, Curitiba, Brazil

    Henrique Y. Shishido

  2. Institute of Mathematics and Computer Science, University of São Paulo, São Paulo, Brazil

    Júlio C. Estrella & Claudio F. M. Toledo

  3. University of Derby, Derby, UK

    Stephan Reiff-Marganiec

Authors
  1. Henrique Y. Shishido
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Júlio C. Estrella
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Claudio F. M. Toledo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stephan Reiff-Marganiec
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Henrique Y. Shishido.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shishido, H.Y., Estrella, J.C., Toledo, C.F.M. et al. Optimizing security and cost of workflow execution using task annotation and genetic-based algorithm. Computing 103, 1281–1303 (2021). https://doi.org/10.1007/s00607-021-00943-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-021-00943-9

Keywords

Search

Navigation