Skip to main content
SpringerLink
Log in

Enhancing service maintainability by monitoring and auditing SLA in cloud computing

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Enforcing Service Level Agreements (SLA) on service provisioning is a challenge in cloud computing environments. This paper proposes an architecture for multiparty (provider and client) auditing in cloud computing to identify SLA deviations. The architecture uses inspectors (software agents) and an independent auditor (third party) to collect SLA metrics from these parties. Privacy is preserved by using the separation of duties for all associated entities (inspectors and auditors). Additionally, service computing surges are automatically detected and handled using machine learning, avoiding performance bottlenecks and misinterpretation of measured SLA items. Thus, this paper improves service maintainability by avoiding service design changes when the service faces performance issues.

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

Similar content being viewed by others

Notes

  1. www.cs.waikato.ac.nz/ml/weka/.

References

  1. Jung, J.J.: Service chain-based business alliance formation in service-oriented architecture. Expert Syst. Appl. 38(3), 2206–2211 (2011)

    Article  Google Scholar 

  2. Perepletchikov, M., Ryan, C., Frampton, K., Tari, Z.: Coupling metrics for predicting maintainability in service-oriented designs. In: Software Engineering Conference, pp. 329–340 (2007)

  3. Marcon, A.L., Santin, A.O., Stihler, M., Bachtold, J.: A UCONABC resilient authorization evaluation for cloud computing. IEEE Trans. Parallel Distrib. Syst. 25(2), 457–467 (2014)

    Article  Google Scholar 

  4. Slimani, S., Hamrouni, T., Charrada, F.B.: Service-oriented replication strategies for improving quality-of-service in cloud computing a survey. Clust Comput. (2020). https://doi.org/10.1007/s10586-020-03108-z

    Article  Google Scholar 

  5. Masdari, M., Khoshnevis, A.: A survey and classification of the workload forecasting methods in cloud computing. Clust. Comput. 23, 1–26 (2019)

    Google Scholar 

  6. Peng, G., Wang, H., Dong, J., Zhang, H.: Knowledge-based resource allocation for collaborative simulation development in a multitenant cloud computing environment. IEEE Trans. Serv. Comput. 11(2), 306–317 (2018)

    Article  Google Scholar 

  7. Baldan, F.J., Ramírez-Gallego, S., Bergmeir, C., Herrera, F., Benítez, J.M.: A forecasting methodology for workload forecasting in cloud systems. IEEE Trans. Cloud Comput. 6, 929–941 (2018)

    Article  Google Scholar 

  8. Felici-Castell, S., Segura-Garcia, J., Garcia-Pineda, M.: Adaptive QoE-based architecture on cloud mobile media for live streaming. Clust. Comput. 22(4), 1–14 (2019)

    Google Scholar 

  9. Fitó, O., Guitart, J.: Business-driven management of infrastructure-level risks in Cloud providers. Future Gener. Comput. Syst. 32, 41–53 (2014)

    Article  Google Scholar 

  10. Vlachopapadopoulos, K.P., González, R.S., Dimolitsas, I., Dechouniotis, D., Ferrer, A.J., Papavassiliou, S.: Collaborative SLA and reputation-based trust management in cloud federations. Future Gener. Comput. Syst. 100, 498–512 (2019)

    Article  Google Scholar 

  11. Rossem, S.V., Tavernier, W., Colle, D., Pickavet, M., Demeester, P.: Profile-based resource allocation for virtualized network functions. IEEE Trans. Netw. Serv. Manag. 16, 1374–1388 (2019)

    Article  Google Scholar 

  12. Liang, C., Hiremagalore, S., Stavrou, A., Rangwala, H.: Predicting network response times using social information. In: 2011 International Conference on Advances in Social Networks Analysis and Mining, pp. 527–531 (2011)

  13. Dabbagh, M., Hamdaoui, B., Guizani, M., Rayes, A.: An energy-efficient VM prediction migration framework for overcommitted clouds. IEEE Trans. Cloud Comput. 6, 955–966 (2018)

    Article  Google Scholar 

  14. Absa, S., Benedict, S., Kumar, A.: Monitoring IaaS using various cloud monitors. Clust. Comput. 22(1), 1–13 (2019)

    Article  Google Scholar 

  15. Walraven, S., Van Landuyt, D., Truyen, E., Handekyn, K., Joosen, W.: Efficient customization of multitenant Software-as-a-Service applications with service lines. J. Syst. Softw. 91, 48–62 (2014)

    Article  Google Scholar 

  16. Yau, S.S., An, H.G.: Software engineering meets services and cloud computing. Computer (Long Beach Calif.) 44(October), 47–53 (2011)

    Google Scholar 

  17. Cai, F., Zhu, N., He, J., Mu, P., Li, W., Yu, Y.: Survey of access control models and technologies for cloud computing. Clust. Comput. 22, 1–12 (2019)

    Google Scholar 

  18. Masdari, M., Khezri, H.: Efficient VM migrations using forecasting techniques in cloud computing: a comprehensive review. Clust. Comput. 23, 1–30 (2020)

    Article  Google Scholar 

  19. Li, N., Jiang, H., Feng, D., Shi, Z.: Storage sharing optimization under constraints of SLO compliance and performance variability. IEEE Trans. Serv. Comput. 12, 58–72 (2019)

    Article  Google Scholar 

  20. Klemperer, P.F., Jeon, H.Y., Payne, B.D., Hoe, J.C.: High-performance memory snapshotting for real-time, consistent, hypervisor-based monitors. IEEE Trans. Dependable Secure Comput. 17, 518–535 (2020)

    Google Scholar 

  21. Vicentini, C., Santin, A., Viegas, E., Abreu, V.: A machine learning auditing model for detection of multi-tenancy issues within tenant domain. In: 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 543–552 (2018)

  22. Borhani, A.H., Hung, T., Lee, B.-S., Qin, Z.: Power-network aware VM migration heuristics for multi-tier web applications. Clust. Comput. 22(3), 757–782 (2019)

    Article  Google Scholar 

  23. Skene, J., Raimondi, F., Emmerich, W.: Service-level agreements for electronic services. IEEE Trans. Softw. Eng. 36(2), 288–304 (2010)

    Article  Google Scholar 

  24. Tomanek, O., Mulinka, P., Kencl, L.: Multidimensional cloud latency monitoring and evaluation. Comput. Netw. 107, 104–120 (2016)

    Article  Google Scholar 

  25. Wang, Y.H., Wu, I.C.: Achieving high and consistent rendering performance of Java AWT/Swing on multiple platforms. Softw. Pract. Exp. 39(7), 701–736 (2009)

    Google Scholar 

  26. Xie, R., Gamble, R.: A tiered strategy for auditing in the cloud. In: Proceedings—2012 IEEE 5th International Conference on Cloud Computing CLOUD 2012, pp. 945–946 (2012)

  27. Doelitzscher,F., Fischer,C., Moskal,D., Reich,C., Knahl,M., Clarke,N.: ”Validating cloud infrastructure changes by cloud audits”, Proc. - 2012 IEEE 8th World Congr. Serv. Serv. 2012, pp. 377–384, (2012)

  28. Li, L., Xu, L., Li, J., Zhang, C.: Study on the third-party audit in cloud storage service. In: Proceedings—2011 International Conference on Cloud and Service Computing CSC 2011, pp. 220–227 (2011)

  29. Liu, J., Xian, M., Fu, S., Huang, K.: Securing the cloud storage audit service: defending against frame and collude attacks of third party auditor. IET Commun. 8(12), 2106–2113 (2014)

    Article  Google Scholar 

  30. Bodik, P., Griffith, R., Sutton, C., Fox, A., Jordan, M.I., Patterson, D.A.: Statistical machine learning makes automatic control practical for Internet datacenters. In: HotCloud, p. 12 (2009)

  31. Terekhov, D., Tran, T.T., Down, D.G., Beck, J.C.: Integrating queueing theory and scheduling for dynamic scheduling problems. J. Artif. Intell. Res. 50, 535–572 (2014)

    Article  MathSciNet  Google Scholar 

  32. Bottou, L.: From machine learning to machine reasoning. Mach. Learn. 94, 15 (2014)

    Article  MathSciNet  Google Scholar 

  33. Bodík, P., Fox, A., Franklin, M.J., Jordan, M.I., Patterson, D.A.: Workload Spikes for Stateful Services. University of California, Berkeley (2009)

    Google Scholar 

  34. Emeakaroha, V.C., Netto, M.A.S., Calheiros, R.N., Brandic, I., Buyya, R., De Rose, C.A.F.: Towards autonomic detection of SLA violations in Cloud infrastructures. Future Gener. Comput. Syst. 28(7), 1017–1029 (2012)

    Article  Google Scholar 

  35. Zhang, J., Zhao, X.: Efficient chameleon hashing-based privacy-preserving auditing in cloud storage. Clust. Comput. 19, 47–56 (2016)

    Article  Google Scholar 

  36. Qian, P., Liu, Z., He, Q., Zimmermann, R., Wang, X.: Towards automated reentrancy detection for smart contracts based on sequential models. IEEE Access 8, 19685–19695 (2020)

    Article  Google Scholar 

  37. KDE—System Monitor (Ksysguard). https://userbase.kde.org/KSysGuard. Accessed Nov 2020

  38. Viegas, E., Santin, A., Bessani, A., Neves, N.: BigFlow: real-time and reliable anomaly-based intrusion detection for high-speed networks. Future Gener. Comput. Syst. 93, 473–485 (2019)

    Article  Google Scholar 

  39. Segalin, D., Santin, A.O., Marynowski, J.E., Segalin, L., Segalin, L.: An approach to deal with processing surges in cloud computing. In: 2015 IEEE 39th Annual Computer Software and Application Conference, pp. 897–905 (2015)

  40. Uriarte, R.B., De Nicola, R., Scoca, V., Tiezzi, F.: Defining and guaranteeing dynamic service levels in clouds. Future Gener. Comput. Syst. 99, 27–40 (2019)

    Article  Google Scholar 

  41. ElasticSearch—Open Source Search and Analytics. https://www.elastic.co/. Accessed Nov 2020

  42. Eucalyptus Cloud Platform. https://github.com/eucalyptus/eucalyptus. Accessed Nov 2020

Download references

Acknowledgements

This work is partially sponsored by the Brazilian National Council for Scientific and Technological Development (CNPq), Grant 430972/2018-0.

Author information

Authors and Affiliations

  1. Pontifical Catholic University of Parana, Curitiba, 80215-901, Brazil

    Eduardo Viegas, Altair Santin, Juliana Bachtold, Darlan Segalin & Maicon Stihler

  2. Federal Center for Technological Education of Minas Gerais, Leopoldina, 36700-001, Brazil

    Arlindo Marcon

  3. Federal University of Parana, Curitiba, 80060-000, Brazil

    Carlos Maziero

Authors
  1. Eduardo Viegas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Altair Santin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juliana Bachtold
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Darlan Segalin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maicon Stihler
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Arlindo Marcon
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Carlos Maziero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eduardo Viegas.

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

Viegas, E., Santin, A., Bachtold, J. et al. Enhancing service maintainability by monitoring and auditing SLA in cloud computing. Cluster Comput 24, 1659–1674 (2021). https://doi.org/10.1007/s10586-020-03209-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-020-03209-9

Keywords

Search

Navigation