Skip to main content
SpringerLink
Log in

Efficient cloud service ranking based on uncertain user requirements

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In a cloud computing environment, there are many providers offering various services of different quality attributes. Selecting a cloud service that meets user requirements from such a large number of cloud services is a complex and time-consuming process. At the same time, user requirements are sometimes described as uncertain (sets or intervals), something which should be taken into account while selecting cloud services. This paper proposes an efficient method for ranking cloud services while accounting for uncertain user requirements. For this purpose, a requirement interval is defined to fulfill uncertain user requirements. Since there are a large number of cloud services, the services falling outside the requirement interval are filtered out. Finally, the analytic hierarchy process is employed for ranking. The results evaluate the proposed method in terms of optimality of ranking, scalability, and sensitivity analyses. According to the test results, the proposed method outperforms the previous methods.

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

References

  1. Abdel-Basset, M., Mohamed, M., Chang, V.: NMCDA: a framework for evaluating cloud computing services. Future Gener. Comput. Syst. 86, 12–29 (2018)

    Article  Google Scholar 

  2. Al-Masri, E., Mahmoud, Q.H.: Discovering the best web service. In: Proceedings of the 16th International Conference on World Wide Web, pp. 1257–1258 (2007)

  3. Alelaiwi, A.: Evaluating distributed IoT databases for edge/cloud platforms using the analytic hierarchy process. J. Parallel Distrib. Comput. 124, 41–46 (2019)

    Article  Google Scholar 

  4. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput Syst. 25(6), 599–616 (2009)

    Article  Google Scholar 

  5. Chang, D.Y.: Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 95(3), 649–655 (1996)

    Article  Google Scholar 

  6. Christopher Frey, H., Patil, S.R.: Identification and review of sensitivity analysis methods. Risk Anal. 22(3), 553–578 (2002)

    Article  Google Scholar 

  7. Devi, R., Shanmugalakshmi, R.: Cloud providers ranking and selection using quantitative and qualitative approach. Comput. Commun. 154, 370–379 (2020)

    Article  Google Scholar 

  8. Dyer, J.S., Fishburn, P.C., Steuer, R.E., Wallenius, J., Zionts, S.: Multiple criteria decision making, multiattribute utility theory: the next ten years. Manag. Sci. 38(5), 645–654 (1992)

    Article  Google Scholar 

  9. Fan, J., Yu, S., Yu, M., Chu, J., Tian, B., Li, W., Wang, H., Hu, Y., Chen, C.: Optimal selection of design scheme in cloud environment: a novel hybrid approach of multi-criteria decision-making based on F-ANP and F-QFD. J. Intell. Fuzzy Syst. 38(3), 3371–3388 (2020)

    Article  Google Scholar 

  10. Garg, R.: MCDM-based parametric selection of cloud deployment models for an academic organization. IEEE Trans. Cloud Comput. (2020). https://doi.org/10.1109/TCC.2020.2980534

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Gireesha, O., Somu, N., Krithivasan, K., Shankar Sriram, V.S.: IIVIFS-WASPAS: an integrated multi-criteria decision-making perspective for cloud service provider selection. Future Gener. Comput. Syst. 103, 91–110 (2020)

    Article  Google Scholar 

  13. Goraya, M.S., Singh, D., et al.: A comparative analysis of prominently used MCDM methods in cloud environment. J. Supercomput. 77(4), 3422–3449 (2021)

    Article  Google Scholar 

  14. Han, S.M., Hassan, M.M., Yoon, C.W., Huh, E.N.: Efficient service recommendation system for cloud computing market. In: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, pp. 839–845 (2009)

  15. Iosup, A., Ostermann, S., Yigitbasi, M.N., Prodan, R., Fahringer, T., Epema, D.: Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans. Parallel Distrib. Syst. 22(6), 931–945 (2011)

    Article  Google Scholar 

  16. Ishizaka, A., Labib, A.: Analytic hierarchy process and expert choice: benefits and limitations. OR Insights 22(4), 201–220 (2009)

    Article  Google Scholar 

  17. Izadpanah, S., Vahdat-Nejad, H., Saadatfar, H.: A framework for ranking ubiquitous computing services by AHP analysis. Int. J. Model. Simul. Sci. Comput. 9(04), 1850023 (2018)

    Article  Google Scholar 

  18. Jahani, A., Khanli, L.M.: Cloud service ranking as a multi objective optimization problem. J. Supercomput. 72(5), 1897–1926 (2016)

    Article  Google Scholar 

  19. Jiang, Y., Tao, D., Liu, Y., Sun, J., Ling, H.: Cloud service recommendation based on unstructured textual information. Future Gener. Comput. Syst. 97, 387–396 (2019)

    Article  Google Scholar 

  20. Kumar, R.R., Mishra, S., Kumar, C.: Prioritizing the solution of cloud service selection using integrated MCDM methods under fuzzy environment. J. Supercomput. 73(11), 4652–4682 (2017)

    Article  Google Scholar 

  21. 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. Clust. Comput. 24, 1–17 (2020)

    Google Scholar 

  22. Kwon, H.K., Seo, K.K.: A decision-making model to choose a cloud service using fuzzy AHP. Adv. Sci. Technol. Lett. 35(1), 93–96 (2013)

    Google Scholar 

  23. Lee, S., Seo, K.K.: A hybrid multi-criteria decision-making model for a cloud service selection problem using BSC, fuzzy Delphi method and fuzzy AHP. Wirel. Pers. Commun. 86(1), 57–75 (2016)

    Article  Google Scholar 

  24. Li, A., Yang, X., Kandula, S., Zhang, M.: CloudCmp: comparing public cloud providers. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, pp. 1–14 (2010)

  25. Li, J., Squicciarini, A.C., Lin, D., Sundareswaran, S., Jia, C.: MMB cloud-tree: authenticated index for verifiable cloud service selection. IEEE Trans. Dependable Secure Comput. 14(2), 185–198 (2015)

    Article  Google Scholar 

  26. Lin, D., Squicciarini, A.C., Dondapati, V.N., Sundareswaran, S.: A cloud brokerage architecture for efficient cloud service selection. IEEE Trans. Serv. Comput. 12(1), 144–157 (2016)

    Article  Google Scholar 

  27. Martin, A., Lakshmi, T.M., Venkatesan, V.P.: A study on evaluation metrics for multi criteria decision making (MCDM) methods—TOPSIS, COPRAS and GRA. Int. J. Comput. Algorithm 7(01), 29–37 (2018)

    Article  Google Scholar 

  28. Oriol, M., Marco, J., Franch, X.: Quality models for web services: a systematic mapping. Inf. Softw. Technol. 56(10), 1167–1182 (2014)

    Article  Google Scholar 

  29. Repschlaeger, J., Wind, S., Zarnekow, R., Turowski, K.: Decision model for selecting a cloud provider: a study of service model decision priorities. In: Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, 15–17 August 2013 (2013)

  30. Ribas, M., Furtado, C., de Souza, J.N., Barroso, G.C., Moura, A., Lima, A.S., Sousa, F.R.: A Petri net-based decision-making framework for assessing cloud services adoption: the use of spot instances for cost reduction. J. Netw. Comput. Appl. 57, 102–118 (2015)

    Article  Google Scholar 

  31. Saaty, T.L.: Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1(1), 83–98 (2008)

    Google Scholar 

  32. Saravanan, M., Aramudhan, M., Pandiyan, S.S., Avudaiappan, T.: Priority based prediction mechanism for ranking providers in federated cloud architecture. Clust. Comput. 22(4), 9815–9823 (2019)

    Article  Google Scholar 

  33. Shivakumar, U., Ravi, V., Gangadharan, G.: Ranking cloud services using fuzzy multi-attribute decision making. In: 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8. IEEE (2013)

  34. Siegel, J., Perdue, J.: Cloud services measures for global use: the service measurement index (SMI). In: 2012 Annual SRII Global Conference, pp. 411–415. IEEE (2012)

  35. Smarandache, F.: Neutrosophy: Neutrosophic Probability, Set, and Logic: Analytic Synthesis and Synthetic Analysis. American Research Press (1998)

  36. Somohano-Murrieta, J.C.B., Ocharán-Hernández, J.O., Sánchez-García, A.J., de los Ángeles Arenas-Valdés, M.: Requirements prioritization techniques in the last decade: a systematic literature review. In: 2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT), pp. 11–20. IEEE (2020)

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  39. Tajvidi, M., Ranjan, R., Kolodziej, J., Wang, L.: Fuzzy cloud service selection framework. In: 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet), pp. 443–448. IEEE (2014)

  40. Tchernykh, A., Schwiegelsohn, U., Alexandrov, V., Talbi, E.: Towards understanding uncertainty in cloud computing resource provisioning. Procedia Comput. Sci. 51, 1772–1781 (2015)

    Article  Google Scholar 

  41. Tiwari, R.K., Kumar, R.: A framework for prioritizing cloud services in neutrosophic environment. J. King Saud Univ. Comput. Inf. Sci. (2020). https://doi.org/10.1016/j.jksuci.2020.05.009

    Article  Google Scholar 

  42. Tran, V.X., Tsuji, H., Masuda, R.: A new QoS ontology and its QoS-based ranking algorithm for web services. Simul. Model. Pract. Theory 17(8), 1378–1398 (2009)

    Article  Google Scholar 

  43. Wibowo, S., Deng, H.: Multi-criteria group decision making for evaluating the performance of e-waste recycling programs under uncertainty. Waste Manag. 40, 127–135 (2015)

    Article  Google Scholar 

  44. Wibowo, S., Deng, H., Xu, W.: Evaluation of cloud services: a fuzzy multi-criteria group decision making method. Algorithms 9(4), 84 (2016)

    Article  MathSciNet  Google Scholar 

  45. Youssef, A.E.: An integrated MCDM approach for cloud service selection based on TOPSIS and BWM. IEEE Access 8, 71851–71865 (2020)

    Article  Google Scholar 

  46. Yu, P.L.: Multiple-Criteria Decision Making: Concepts, Techniques, and Extensions, vol. 30. Springer, New York (2013)

    Google Scholar 

  47. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

    Mohammad Hossein Nejat & Homayun Motameni

  2. Department of Computer Engineering, University of Birjand, Birjand, Iran

    Hamed Vahdat-Nejad

  3. Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran

    Behnam Barzegar

Authors
  1. Mohammad Hossein Nejat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Homayun Motameni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamed Vahdat-Nejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Behnam Barzegar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Homayun Motameni.

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

Nejat, M.H., Motameni, H., Vahdat-Nejad, H. et al. Efficient cloud service ranking based on uncertain user requirements. Cluster Comput 25, 485–502 (2022). https://doi.org/10.1007/s10586-021-03418-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03418-w

Keywords

Search

Navigation