Skip to main content
SpringerLink
Log in

A model-based approach for formal verification and performance analysis of dynamic load-balancing protocols in cloud environment

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Cloud computing is a new technology, providing different online resources and services to users. Load balancing has become an interesting research area in this field. A few existing scheduling algorithms can maintain load balancing and provide better strategies through efficient job scheduling and resource allocation techniques as well. In order to gain maximum profits with optimized load balancing algorithms, it is necessary to guarantee a service level along dimensions such as performance, availability and reliability. In this work, we propose a Model-based approach to study and analyze centralized and distributed dynamic load balancing protocols in the Cloud. The formal verification of different properties of the studied protocols has been performed automatically and a performance analysis allowing their comparison is also provided. In this paper, we show how aspects of performance, resource consumption, and reliability of the Cloud can be formally modeled, verified and analyzed using a component-based architecture.

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.

Institutional subscriptions

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. Ghomi, E.J., Rahmani, A.M., Qader, N.N.: Load-balancing algorithms in cloud computing: a survey. J. Netw. Comput. Appl. 88, 50–71 (2017)

    Article  Google Scholar 

  2. Ali, O., et al.: Cloud computing-enabled healthcare opportunities, issues, and applications: a systematic review. Int. J. Inf. Manag. 43, 146–158 (2018)

    Article  Google Scholar 

  3. Ferretti, L., et al.: A symmetric cryptographic scheme for data integrity verification in cloud databases. Inf. Sci. 422, 497–515 (2018)

    Article  MathSciNet  Google Scholar 

  4. Arunarani, A.R., Manjula, D., Vijayan, S.: Task scheduling techniques in cloud computing: a literature survey. Future Gener. Comput. Syst. 91, 407–415 (2019)

    Article  Google Scholar 

  5. Jena, R.K.: Task scheduling in cloud environment: a multi-objective ABC framework. J. Inf. Optim. Sci. 38(1), 1–19 (2017)

    MathSciNet  Google Scholar 

  6. Jyoti, A., Shrimali, M.: Dynamic provisioning of resources based on load balancing and service broker policy in cloud computing. Clust. Comput. 23(1), 377–395 (2020)

    Article  Google Scholar 

  7. Lin, W., et al.: A cloud server energy consumption measurement system for heterogeneous cloud environments. Inf. Sci. 468, 47–62 (2018)

    Article  Google Scholar 

  8. Deepa, T., Cheelu, D.: A comparative study of static and dynamic load balancing algorithms in cloud computing. In: International Conference on Energy. Communication, Data Analytics and Soft Computing (ICECDS). IEEE (2017)

  9. Klaithem, N.A.: A survey of load balancing in cloud computing: Challenges and algorithms. In: Second Symposium on Network Cloud Computing and Applications, pp. 137–142. IEEE (2012)

  10. Thakur, A., Major, S.G.: A taxonomic survey on load balancing in cloud. J. Netw. Comput. Appl. 98, 43–57 (2017)

    Article  Google Scholar 

  11. Basu, A., et al.: Rigorous component-based system design using the BIP framework. IEEE Softw. 28(3), 41–48 (2011)

    Article  Google Scholar 

  12. Dellabani, M., Legay, A., Bensalem, S.: SBIP 2.0: statistical model checking stochastic real-time systems. Autom. Technol. Verif. Anal. 536–542 (2018)

  13. Nouri, A., et al.: Statistical model checking QoS properties of systems with SBIP. Int. J. Softw. Tools Technol. Transf. 17(2), 171–185 (2015)

    Article  Google Scholar 

  14. Hafaiedh, I.B. et al.: Formal distributed model for the verification of job-scheduling in cloud environments. In: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA). IEEE (2017)

  15. Gawanmeh, A., Alomari, A.: Challenges in formal methods for testing and verification of cloud computing systems. Scalable Comput. 16(3), 321–332 (2015)

    Google Scholar 

  16. Bugingo, E. et al.: Towards decomposition based multi-objective workflow scheduling for big data processing in clouds. Clust. Comput. 1–25 (2020)

  17. Wang, L., Gelenbe, E.: Adaptive dispatching of tasks in the cloud. IEEE Trans. Cloud Comput. 6, 33–45 (2015)

    Article  Google Scholar 

  18. Barbierato, E., et al.: Exploiting CloudSim in a multiformalism modeling approach for cloud based systems. Simul. Model. Pract. Theory 93, 133–147 (2019)

    Article  Google Scholar 

  19. Cai, H., Hao, W.: An improved formalization analysis approach to determine schedulability of global multiprocessor scheduling based on symbolic safety analysis and statistical model checking in smartphone systems. Clust. Comput. 22(2), 2543–2554 (2019)

    Article  Google Scholar 

  20. Ishakian, V. et al.: Formal verification of SLA transformations. In: 2011 IEEE World Congress on Services. IEEE (2011)

  21. Blanchard, A. et al.: A case study on formal verification of the Anaxagoros hypervisor paging system with Frama-C. In: International Workshop on Formal Methods for Industrial Critical Systems. Springer (2015)

  22. Gao, J. et al.: SaaS performance and scalability evaluation in clouds. In: Proceedings of 2011 IEEE 6th International Symposium on Service Oriented System Engineering (SOSE), pp. 61–71. IEEE (2011)

  23. Souri, A., et al.: A hybrid formal verification approach for QoS-aware multi-cloud service composition. Clust. Comput. 23(4), 2453–2470 (2020)

    Article  Google Scholar 

  24. Pereverzeva, I. et al.: Formal modelling of resilient data storage in cloud. In: International Conference on Formal Engineering Methods. Springer, Berlin (2013)

    Google Scholar 

  25. Chen, J. et al.: A formal model for resource protections in web service applications. In: 2012 international conference on cloud and service computing. IEEE (2012)

  26. Hao, J., et al.: vTRUST: a formal modeling and verification framework for virtualization systems. In: International Conference on Formal Engineering Methods. Springer, Berlin (2013)

  27. Bleikertz, S., Thomas, G.: A virtualization assurance language for isolation and deployment. In: 2011 IEEE International Symposium on Policies for Distributed Systems and Networks. IEEE (2011)

  28. Kikuchi, S., Yasuhide, M.: Performance modeling of concurrent live migration operations in cloud computing systems using Prism probabilistic model checker. In: 2011 IEEE 4th International Conference on Cloud Computing. IEEE (2011)

  29. Albert, E., et al.: Formal modeling and analysis of resource management for cloud architectures: an industrial case study using real-time ABS. Serv. Oriented Comput. Appl. 8(4), 323–339 (2014)

    Article  Google Scholar 

  30. Calheiros, R.N., et al.: CloudSim: a Toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software 41(1), 23–50 (2011)

    MathSciNet  Google Scholar 

  31. Lin, W., et al.: Multi-resource scheduling and power simulation for cloud computing. Inf. Sci. 397, 168–186 (2017)

    Article  Google Scholar 

  32. Neelima, P., Reddy, A.R.M.: An efficient load balancing system using adaptive dragonfly algorithm in cloud computing. Clust. Comput. 1–9 (2020)

  33. Calheiros, R.N., et al.: EMUSIM: an integrated emulation and simulation environment for modeling, evaluation, and validation of performance of cloud computing applications. Software 43(5), 595–612 (2013)

    Google Scholar 

  34. Shetty, S.M., Shetty, S.: Analysis of load balancing in cloud data centers. J. Ambient Intell. Hum. Comput. 1–9 (2019)

  35. Liu, G., Shen, H., Wang, H.: Towards long-view computing load balancing in cluster storage systems. IEEE Trans. Parallel Distrib. Syst. 28, 1770–1784 (2017)

    Article  Google Scholar 

  36. Jarraya, Y.: Cloud calculus: Security verification in elastic cloud computing platform. In: International Conference on Collaboration Technologies and Systems (CTS), pp. 447–454. IEEE (2012)

  37. Hafaiedh, I.B., Slimane, M.B., Robbana, R.: A formal model for the analysis and verification of a pre-emptive round-robin arbiter. Int. J. Crit. Comput.-Based Syst. 8(2), 169–192 (2018)

    Article  Google Scholar 

  38. Falcone, Y. et al.: Runtime verification of component-based systems. In: International Conference on Software Engineering and Formal Methods. Springer, Berlin (2011)

  39. Ben-Hafaiedh, I., Graf, S., Quinton, S.: Building distributed controllers for systems with priorities. J. Logic Algebraic Program. 80(3–5), 194–218 (2011)

    Article  MathSciNet  Google Scholar 

  40. Bliudze, S., Sifakis, J.: The algebra of connectors: Structuring interaction in BIP. IEEE Trans. Comput. 57(10), 1315–1330 (2008)

    Article  MathSciNet  Google Scholar 

  41. Yang, K., et al.: Towards efficient resource on-demand in grid computing. ACM SIGOPS Oper. Syst. Rev. 37(2), 37–43 (2003)

    Article  Google Scholar 

  42. Liu, L., Deyu, Q.: An independent task scheduling algorithm in heterogeneous multi-core processor environment. In: 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE (2018)

  43. Choi, D., Chung, K.S., Shon, J.: An improvement on the weighted least-connection scheduling algorithm for load balancing in web cluster systems. In: Grid and Distributed Computing, Control and Automation, pp. 127–134. Springer, Berlin (2010)

  44. Ren, X., Rongheng, L., Hua, Z.: A dynamic load balancing strategy for cloud computing platform based on exponential smoothing forecast. In: 2011 IEEE International Conference on Cloud Computing and Intelligence Systems (2011)

  45. Lim, J.W., Hoong, P.K., Yeoh, E.T.: Heuristic neighbor selection algorithm for decentralized load balancing in clustered heterogeneous computational environment. In: 2012 14th International Conference on Advanced Communication Technology (ICACT), pp. 1215–1219. IEEE (2012)

Download references

Author information

Authors and Affiliations

  1. University of Tunis El Manar (UTM), ISI, 1002, Tunis, Tunisia

    Imene Ben Hafaiedh

  2. University of Tunis El Manar (UTM), Faculty of Sciences of Tunis (FST), 2092, Tunis, Tunisia

    Roua Ben Hamouda

  3. National Institute of Applied Science and Technology, University of Carthage (UC), Tunis, Tunisia

    Riadh Robbana

Authors
  1. Imene Ben Hafaiedh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roua Ben Hamouda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Riadh Robbana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Imene Ben Hafaiedh.

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

Ben Hafaiedh, I., Ben Hamouda, R. & Robbana, R. A model-based approach for formal verification and performance analysis of dynamic load-balancing protocols in cloud environment. Cluster Comput 24, 2977–2994 (2021). https://doi.org/10.1007/s10586-021-03305-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03305-4

Keywords

Search

Navigation