Skip to main content
Log in

CCS-OSSR: A framework based on Hybrid MCDM for Optimal Service Selection and Ranking of Cloud Computing Services

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

With the exponential proliferation of cloud services, the decision of trustworthy cloud service selection has become tremendously challenging nowadays. It demands an accurate decision system to carry out a comprehensive assessment of cloud services from various aspects. The immense complexity and limitations of existing approaches reduce the credibility of the service selection process; thus, further research is necessitated to produce more authentic service selection results. In this regard, this paper proposes a novel framework called Optimal Service Selection and Ranking of Cloud Computing Services (CCS-OSSR), which allows cloud customers to compare available service choices based on QoS (Quality of Criteria) criteria. The CCS-OSSR utilizes a hybrid multi-criteria decision making approach. Best worst method is used to rank and prioritize the QoS criteria and Technique for Order Preference by Similarity to Ideal Solution approach is employed to obtain the final rank of cloud services. To verify the applicability/effectiveness, the proposed methodology validated with the help of comprehensive analysis. In addition, we examine the proposed methodology in term of sensitivity analysis and comparative analysis. The outcomes of sensitivity and comparative analysis show that the proposed approach requires less pairwise comparisons and can provide better consistent solution against existing solutions.

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

Access this article

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

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. Abdullah, A.M., Ali, H.A., Haikal, A.Y.: A reliable, topsis-based multi-criteria, and hierarchical load balancing method for computational grid. Cluster Comput. 22(4), 1085–1106 (2019)

    Article  Google Scholar 

  3. Al-Janabi, S., Al-Shourbaji, I., Shojafar, M., Abdelhag, M.: Mobile cloud computing: challenges and future research directions. In: 2017 10th International Conference on Developments in eSystems Engineering (DeSE), IEEE, pp. 62–67 (2017)

  4. Al-Janabi, S., Alkaim, A.F., Adel, Z.: An innovative synthesis of deep learning techniques (dcapsnet & dcom) for generation electrical renewable energy from wind energy. Soft Comput. 24, 10943–10962 (2020)

    Article  Google Scholar 

  5. Al-Masri, E., Mahmoud, Q.H.: The qws dataset (2008)

  6. Alabool, H.M., Mahmood, A.K.B.: A novel evaluation framework for improving trust level of infrastructure as a service. Cluster Computing 19(1), 389–410 (2016)

    Article  Google Scholar 

  7. Alhanahnah, M., Bertok, P., Tari, Z., Alouneh, S.: Context-aware multifaceted trust framework for evaluating trustworthiness of cloud providers. Future Gener. Comput. Syst. 79, 488–499 (2018)

    Article  Google Scholar 

  8. Al\(\_\)Janabi, S., Hussein, N.Y.: The reality and future of the secure mobile cloud computing (smcc): survey. In: International Conference on Big Data and Networks Technologies, pp. 231–261. Springer, New York (2019)

  9. Baranwal, G., Vidyarthi, D.P.: A cloud service selection model using improved ranked voting method. Concurr. Comput. Pract. Exp. 28(13), 3540–3567 (2016)

    Article  Google Scholar 

  10. Boussoualim, N., Aklouf, Y.: Evaluation and selection of saas product based on user preferences. In: 2015 Third International Conference on Technological Advances in Electrical, pp. 299–308. Electronics and Computer Engineering (TAEECE), IEEE (2015)

  11. CSMIC: Cloud services measures for global use: the service measurement index (smi) (2011)

  12. 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 

  13. Gobi, N., Rathinavelu, A.: Analyzing cloud based reviews for product ranking using feature based clustering algorithm. Cluster Comput. 22(3), 6977–6984 (2019)

    Article  Google Scholar 

  14. Godse, M., Mulik, S.: An approach for selecting software-as-a-service (saas) product. In: IEEE International Conference on Cloud Computing, 2009. CLOUD’09. IEEE, pp 155–158 (2009)

  15. Goraya, M.S., Singh, D., et al.: Satisfaction aware qos-based bidirectional service mapping in cloud environment. Cluster Comput. (2020). https://doi.org/10.1007/s10586-020-03065-7

    Article  Google Scholar 

  16. Gui, Z., Yang, C., Xia, J., Huang, Q., Liu, K., Li, Z., Yu, M., Sun, M., Zhou, N., Jin, B.: A service brokering and recommendation mechanism for better selecting cloud services. PLoS ONE 9(8), e105297 (2014)

    Article  Google Scholar 

  17. Hussain, A., Chun, J., Khan, M.: A novel framework towards viable cloud service selection as a service (cssaas) under a fuzzy environment. Future Gener. Comput. Syst 104, 74–91 (2019)

    Article  Google Scholar 

  18. Hwang, C.L., Yoon, K.: Multiple attribute decision making: methods and applications a state-of-the-art survey, vol. 186. Springer, Brelin (2012)

    MATH  Google Scholar 

  19. Jatoth, C., Gangadharan, G., Fiore, U.: Evaluating the efficiency of cloud services using modified data envelopment analysis and modified super-efficiency data envelopment analysis. Soft. Comput. 21(23), 7221–7234 (2017)

    Article  Google Scholar 

  20. Jatoth, C., Gangadharan, G., Fiore, U., Buyya, R.: Selcloud: a hybrid multi-criteria decision-making model for selection of cloud services. Soft Comput. (2018). https://doi.org/10.1007/s00500-018-3120-2

    Article  Google Scholar 

  21. Karim, R., Ding, C., Miri, A.: An end-to-end qos mapping approach for cloud service selection. In: 2013 IEEE Ninth World Congress on Services, IEEE, pp. 341–348 (2013)

  22. Khan, A.A., Shameem, M., Kumar, R.R., Hussain, S., Yan, X.: Fuzzy ahp based prioritization and taxonomy of software process improvement success factors in global software development. Appl. Soft Comput. 83, 105648 (2019)

    Article  Google Scholar 

  23. Khanam, R., Kumar, R.R., Kumar, C.: Qos based cloud service composition with optimal set of services using pso. In: 2018 4th International Conference on Recent Advances in Information Technology (RAIT), IEEE, pp 1–6 (2018a)

  24. Khanam, R., Kumar, R.R., Kumari, B.: A novel approach for cloud service composition ensuring global qos constraints optimization. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, pp 1695–1701 (2018b)

  25. Kumar, R.R., Kumar, C.: Designing an efficient methodology based on entropy-topsis for evaluating efficiency of cloud services. In: Proceedings of the 7th International Conference on Computer and Communication Technology, pp. 117–122 (2017)

  26. Kumar, R.R., Shameem, M., Khanam, R., Kumar, C.: A hybrid evaluation framework for qos based service selection and ranking in cloud environment. In: 2018 15th IEEE India Council International Conference (INDICON), IEEE, pp. 1–6 (2018)

  27. Lu, L., Yuan, Y.: A novel topsis evaluation scheme for cloud service trustworthiness combining objective and subjective aspects. J. Syst. Softw. 143, 71–86 (2018)

    Article  Google Scholar 

  28. Ma, H., Hu, Z., Li, K., Zhang, H.: Toward trustworthy cloud service selection: a time-aware approach using interval neutrosophic set. J. Parallel Distrib. Comput. 96, 75–94 (2016)

    Article  Google Scholar 

  29. Mahdi, M.A., Al\(\_\)Janabi, S.: A novel software to improve healthcare base on predictive analytics and mobile services for cloud data centers. In: International Conference on Big Data and Networks Technologies, pp 320–339. Springer, Berlin (2019)

  30. Mei, Y., Xie, K.: An improved topsis method for metro station evacuation strategy selection in interval type-2 fuzzy environment. Cluster Comput. 22(2), 2781–2792 (2019)

    Article  Google Scholar 

  31. Mell, P., Grance, T., et al.: The nist definition of cloud computing (2011)

  32. Menzel, M., Ranjan, R., Wang, L., Khan, S.U., Chen, J.: Cloudgenius: a hybrid decision support method for automating the migration of web application clusters to public clouds. IEEE Trans. Comput. 64(5), 1336–1348 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  33. Nivethitha, S., Raman, M.G., Gireesha, O., Kannan, K., Sriram, V.S.: An improved rough set approach for optimal trust measure parameter selection in cloud environments. Soft. Comput. 23(22), 11979–11999 (2019)

    Article  Google Scholar 

  34. Panwar, N., Negi, S., Rauthan, M.M.S., Vaisla, K.S.: Topsis-pso inspired non-preemptive tasks scheduling algorithm in cloud environment. Cluster Comput. 22(4), 1379–1396 (2019)

    Article  Google Scholar 

  35. Patiniotakis, I., Verginadis, Y., Mentzas, G.: Pulsar: preference-based cloud service selection for cloud service brokers. J. Internet Serv. Appl. 6(1), 26 (2015)

    Article  Google Scholar 

  36. Qi, L., Dou, W., Chen, J.: Weighted principal component analysis-based service selection method for multimedia services in cloud. Computing 98(1–2), 195–214 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  37. ur Rehman, Z., Hussain, O.K., Hussain, F.K.: IAAS cloud selection using MCDM methods. In: 2012 IEEE Ninth International Conference on e-Business Engineering, pp 246–251. IEEE (2012)

  38. ur Rehman, Z., Hussain, O.K., Hussain, F.K.: Multi-criteria IAAS service selection based on qos history. In: 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA). IEEE, pp. 1129–1135 (2013)

  39. Rezaei, J.: Best-worst multi-criteria decision-making method. Omega 53, 49–57 (2015)

    Article  Google Scholar 

  40. Sahri, S., Moussa, R., Long, D.D., Benbernou, S.: Dbaas-expert: a recommender for the selection of the right cloud database. In: International Symposium on Methodologies for Intelligent Systems, pp. 315–324. Springer, Berlin (2014)

  41. Shameem, M., Kumar, R.R., Nadeem, M., Khan, A.A.: Taxonomical classification of barriers for scaling agile methods in global software development environment using fuzzy analytic hierarchy process. Appl. Soft Comput. 90, 106122 (2020)

    Article  Google Scholar 

  42. Shojafar, M., Canali, C., Lancellotti, R.: A computation-and network-aware energy optimization model for virtual machines allocation. In: Proceedings of the International Conference on Cloud Computing and Services Science (CLOSER 2017), Porto, Portugal, pp 24–26 (2017)

  43. Sidhu, J., Singh, S.: Improved topsis method based trust evaluation framework for determining trustworthiness of cloud service providers. J. Grid Comput. 15(1), 81–105 (2017)

    Article  Google Scholar 

  44. Singh, S., Sidhu, J.: Compliance-based multi-dimensional trust evaluation system for determining trustworthiness of cloud service providers. Future Gener. Comput. Syst. 67, 109–132 (2017)

    Article  Google Scholar 

  45. Souri, A., Rahmani, A.M., Navimipour, N.J., Rezaei, R.: A hybrid formal verification approach for qos-aware multi-cloud service composition. Cluster Comput. (2019). https://doi.org/10.1007/s10586-019-03018-9

    Article  Google Scholar 

  46. Sun, L.: An influence diagram based cloud service selection approach in dynamic cloud marketplaces. Cluster Comput. 22, 7369 (2019)

    Article  Google Scholar 

  47. 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 

  48. Tripathi, A., Pathak, I., Vidyarthi, D.P.: Integration of analytic network process with service measurement index framework for cloud service provider selection. Concurr. Comput. Pract. Exp. 29(12), e4144 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rakesh Ranjan Kumar.

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

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 24, 867–883 (2021). https://doi.org/10.1007/s10586-020-03166-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-020-03166-3

Keywords

Navigation