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Cloud services security-driven evaluation for multiple tenants

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Abstract

Cloud Computing has become a reliable solution for outsourcing business data and operation with its cost-effective and resource-efficient services. A key part of the success of the cloud is the multi-tenancy architecture, where a single instance of a service can be shared between a large number of users, also known as tenants. Service selection for multiple tenants presents a real challenge that has not been properly addressed in the literature so far. Most of the existing cloud services selection approaches are designed for a single-user, and hence are inefficient when applied to the case of a large group of users with different, and often, conflicting requirements. In this paper, we propose a multi-tenant cloud services evaluation framework, whereby service selection is carried out per group of tenants that can belong to different service classes, rather than per a single user. We formulate the cloud services selection for multi-tenants as a complex multi-attribute large-group decision-making (CMALGDM) problem. Skyline method is initially applied to reduce the search space by eliminating the dominated services regardless of tenants’ requirements. Tenants are clustered based on their profiles characterized by different personal, service, and environmental features. Each tenant is assigned a weight to reflect its importance in the decision-making. The weight of a tenant is determined locally based on its closeness to the group decision and globally by combining its local weight with its corresponding cluster weight to reflect its total contribution to the overall decision-making. The final ranking of the alternatives is guided by a dynamic consensus process to reach a final solution with the highest level of agreement. The proposed framework supports multiple types of information, including deterministic data, interval numbers, and fuzzy numbers, to realistically represent the heterogeneity and uncertainty of security information.

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References

  1. J. Bennett, “Why a no-cloud policy will become extinct,” Gartner Available at https://www.gartner.com/smarterwithgartner/cloud-computing-predicts/

  2. Badger, L., Patt-corner, R., Voas, J.: NIST cloud computing synopsis and recommendations. Nist Spec. Publ. 800(146), 81 (2012)

    Google Scholar 

  3. Salesforce. [online]. https://.salesforce.com

  4. Hawedi, M., Talhi, C., Boucheneb, H.: Multi-tenant intrusion detection system for public cloud (MTIDS), vol. 74. Springer, New York (2018)

    Google Scholar 

  5. Chen, X.H.: Complex large-group decision-making methods and application. Press, Beijing, Sci (2009). in Chinese

    Google Scholar 

  6. Garg, S.K., Versteeg, S., Buyya, R.: A framework for ranking of cloud computing services. Futur. Gener. Comput. Syst. 29(4), 1012–1023 (2013)

    Article  Google Scholar 

  7. Kumar, R.R., Kumari, B., Kumar, C.: CCS-OSSR : A framework based on hybrid MCDM for optimal service selection and ranking of cloud computing services”. Cluster Comput. 3, 1–17 (2020)

    Google Scholar 

  8. Ding, S., Yang, S., Zhang, Y., Liang, C., Xia, C.C.C.: Combining QoS prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problems. Knowledge-Based Syst. 56, 216–225 (2014)

    Article  Google Scholar 

  9. Hammadi, A., Hussain, O.K., Dillon, T., Hussain, F.K.: A framework for SLA management in cloud computing for informed decision making. Cluster Comput. 16(4), 961–977 (2013)

    Article  Google Scholar 

  10. Sundara, M.A.S., Avudaiappan, P.T.: Priority-based prediction mechanism for ranking providers in federated cloud architecture. Cluster Comput. 22(s4), 9815–9823 (2019)

    Article  Google Scholar 

  11. Sun, L., Ma, J., Zhang, Y., Dong, H., Hussain, F.K.: Cloud-FuSeR: fuzzy ontology and MCDM based cloud service selection. Futur. Gener. Comput. Syst. 57, 42–55 (2016)

    Article  Google Scholar 

  12. Sun, L., Dong, H., Hussain, O.K., Hussain, F.K., Chang, E.: Cloud service selection: state-of-the-art and future research directions. J. Netw. Comput. Appl. 45(October), 134–150 (2014)

    Article  Google Scholar 

  13. Alabool, H., Kamil, A., Arshad, N., Alarabiat, D.: Cloud service evaluation method-based multi-criteria decision-making: a systematic literature review. J. Syst. Softw. 139, 161–188 (2018)

    Article  Google Scholar 

  14. Taha, A., Trapero, R., Luna, J., Suri, N.: AHP-based quantitative approach for assessing and comparing cloud security. In: Proc. - 2014 IEEE 13th Int. Conf. Trust. Secur. Priv. Comput. Commun. Trust. 2014, pp. 284–291 (2015)

  15. Cloud Controls Matrix. [Online]. https://cloudsecurityalliance.org/group/cloud-controls-matrix/

  16. Modic, J., Trapero, R., Taha, A., Luna, J., Stopar, M., Suri, N.: Novel efficient techniques for real-time cloud security assessment. Comput. Secur. 62, 1–18 (2016)

    Article  Google Scholar 

  17. Halabi, T., Bellaiche, M.: Towards quantification and evaluation of security of Cloud Service Providers. J. Inf. Secur. Appl. 33, 55–65 (2017)

    Google Scholar 

  18. Mohammad, H., Ahmad, A., Bin, K.: A novel evaluation framework for improving trust level of Infrastructure as a Service. Cluster Comput. 19(1), 389–410 (2016)

    Article  Google Scholar 

  19. Wang, Y., He, Q., Zhang, X., Ye, D., Yang, Y.: Efficient QoS-aware service recommendation for multi-tenant service-based systems in cloud. IEEE Trans. Serv. Comput. 1374, 1–14 (2017)

    Google Scholar 

  20. He, Q., Han, J., Yang, Y., Grundy, J., Jin, H.: QoS-driven service selection for multi-tenant SaaS. In: Proc. - 2012 IEEE 5th Int. Conf. Cloud Comput. CLOUD 2012, pp. 566–573 (2012).

  21. Liu, S., Chan, F.T.S., Ran, W.: Decision making for the selection of cloud vendor: an improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Syst. Appl. 55(2016), 37–47 (2016)

    Article  Google Scholar 

  22. Maroc, S., Zhang, J.B.: Cloud services security evaluation for multi-tenants. In: 2019 IEEE Int. Conf. Sig. Proces, Com. and Comp. (ICSPCC) (2019).

  23. Skoutas, D., Sacharidis, D., Simitsis, A., Kantere, V., Sellis, T.K.: Top-k dominant web services under multi-criteria matching. In: EDBT, ACM Inte. Conf. Procd., vol. 360, pp. 898–909 (2009).

  24. Zadeh, L.A.: Fuzzy sets. Inf. Contr. 8, 338–353 (1965)

    Article  MATH  Google Scholar 

  25. Aghajani-Bazzazi, A., Osanloo, M., Karimi, B.: Deriving preference order of open pit mines equipment through MADM methods: application of modified VIKOR method. Expert Syst. Appl. 38(3), 2550–2556 (2011)

    Article  MATH  Google Scholar 

  26. Yoon, K., Hwang, C.L.: TOPSIS (Technique for order preference by similarity to ideal solution)-A multiple attribute decision making (1980)

  27. Yue, Z.: A method for group decision-making based on determining weights of decision-makers using TOPSIS. Appl. Math. Model. 35(4), 1926–1936 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  28. STAR.: Security trust assurance and risk [online]. https://cloudsecurityalliance.org/.

  29. Ma, H., Hu, Z., Yang, L., Song, T.: User feature-aware trustworthiness measurement of cloud services via evidence synthesis for potential users. J. Vis. Lang. Comput. 25(6), 791–799 (2014)

    Article  Google Scholar 

  30. Alrifai, M., Skoutas, D., Risse, T.: Selecting Skyline Services for QoS-Based Web Service Composition, pp. 11–20. ACM, New York (2010)

    Google Scholar 

  31. Saaty, T.L.: Decision Making with Dependence and Feedback: The Analytic Network Process, 2nd edn. RWS Publications, Pittsburgh (2001)

    Google Scholar 

  32. Xu, X.H., Zhang, L.Y., Wan, Q.F.: A variation coefficient similarity measure and its application in emergency group decision-making. Syst. Eng. Proc. 5, 119–124 (2012)

    Article  Google Scholar 

  33. Shannon, E.: The mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)

    Article  MathSciNet  MATH  Google Scholar 

  34. Palomares, I., Estrella, F.J., Martínez, L., Herrera, F.: Consensus under a fuzzy context : taxonomy, analysis framework AFRYCA and experimental case of study. Francisco J. Estrella 20, 252–271 (2014)

    Google Scholar 

  35. Ben-Arieh, D., Chen, Z.: Linguistic-labels aggregation and consensus measure for autocratic decision making using group recommendations. IEEE Trans. Syst. Man, Cybern. Part A Syst Hum. 36(3), 558–568 (2006)

    Article  Google Scholar 

  36. Wang, S., Hsu, C., Liang, Z.: Multi-user web service selection based on multi-QoS prediction. Inf Syst Front. 16, 143–152 (2014)

    Article  Google Scholar 

  37. Wu, X., Fan, Y., Zhang, J. Lin, H., Zhang, J.: QF-RNN: QI-matrix factorization based RNN for time-aware service recommendation. In: Proc. - 2019 IEEE Int. Conf. Serv. Comput. SCC 2019 - Part 2019 IEEE World Congr. Serv., pp. 202–209 (2019).

  38. Yadav, N., Goraya, M.S.: Two-way ranking based service mapping in cloud environment. Futur. Gener. Comput. Syst. 81, 53–66 (2018)

    Article  Google Scholar 

  39. Major, N., Goraya, S., Singh, D.: Satisfaction aware QoS-based bidirectional service mapping in cloud environment. Cluster Comput. 7 (2021).

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Correspondence to Sarah Maroc.

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Maroc, S., Zhang, J.B. Cloud services security-driven evaluation for multiple tenants. Cluster Comput 24, 1103–1121 (2021). https://doi.org/10.1007/s10586-020-03178-z

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