Electronic Commerce Research

, Volume 14, Issue 3, pp 245–270 | Cite as

Quality evaluation and best service choice for cloud computing based on user preference and weights of attributes using the analytic network process

  • Cheol-Rim Choi
  • Hwa-Young Jeong


The quality offered by cloud computing services is becoming an issue of the utmost priority and, regardless of the cloud delivery models, the adoption of cloud services will depend on having the capability to ensure quality of service to the users. However, studies to find and select cloud computing services according to their service quality are still in their infancy. Here, we propose a system that calculates the priority weights for each quality attribute according to the quality preference of a user and the interrelation analysis results between the attributes, and reflects the weights in selecting the cloud computing service. To calculate the quality preference of the user, we applied a pairwise comparison matrix and an eigenvector of the matrix. Through the proposed system, users can easily perform the process of calculating the weights and selecting the best services according to their quality preference. The simulation results show that the weights of the quality attributes and the quality score ranking of the sample cloud computing services vary according to users’ preferences and interrelations between attributes.


Cloud computing Quality evaluation Service choice  Analytic network process Weight Preference 


  1. 1.
    Aragonés-Beltrán, P., Chaparro-González, F., Pastor-Ferrando, J. P., & Rodríguez-Pozo, F. (2010). An ANP-based approach for the selection of photovoltaic solar power plant investment projects. Renewable and Sustainable Energy Reviews, 14(1), 249–264.CrossRefGoogle Scholar
  2. 2.
    Bard, J. F., & Sousk, S. F. (1990). A tradeoff analysis for rough terrain cargo handlers using the AHP: An example of group decision-making. IEEE Transactions on Engineering Management, 37(3), 222–227.CrossRefGoogle Scholar
  3. 3.
    Bard, J. F., & Sousk, S. F. (1990). A tradeoff analysis for rough terrain cargo handlers using the AHP: An example of group decision-making. IEEE Transaction on, Engineering Management, 37(3), 222–228.CrossRefGoogle Scholar
  4. 4.
    Bhattacharya, A., Wanmin, W., & Yang, Z. (2012). Quality of experience evaluation of voice communication: An affect-based approach. Human-centric Computing and Information Sciences. doi: 10.1186/2192-1962-2-7.
  5. 5.
    Boehm, B. W., Brown, J. R., Kaspar, H., Lipow, M., McLeod, G., & Merritt, M. (1978). Characteristics of software quality. Boehm, Barry W: North Holland.Google Scholar
  6. 6.
    Chang, C.-W., Cheng-Ru, W., Lin, C.-T., & Lin, H.-L. (2007). Evaluating digital video recorder systems using analytic hierarchy and analytic network processes. Information Sciences, 177(16), 3383–3396.Google Scholar
  7. 7.
    Comerio, M., Truong, H.-L., Batini, C., Dustdar, S. (2010). Service-oriented data quality engineering and data publishing in the cloud. In 2010 IEEE international conference on service-oriented computing and Applications (SOCA) (pp. 1–6). Perth, WA.Google Scholar
  8. 8.
    Dromey, R. G. (1996). Concerning the chimera [software quality]. IEEE Software, 12(1), 33–43.CrossRefGoogle Scholar
  9. 9.
    European Commission. (2007). INSPIRE network services performance guidelines. INSPIRE Consolidation Team.Google Scholar
  10. 10.
    Gonzalez, J. L., & Marcelnez, R. (2011). Phoenix: Fault-tolerant distributed web storage based on URLs. Journal of Convergence, 2(1), 79–86.Google Scholar
  11. 11.
    Gorla, N., & Lin, S.-C. (2010). Determinants of software quality: A survey of information systems project managers. Information and Software Technology, 52, 602–610.CrossRefGoogle Scholar
  12. 12.
    Goscinski, A., & Brock, M. (2010). Toward dynamic and attribute based publication, discovery and selection for cloud computing. Future Generation Computer Systems, 26(7), 947–970.CrossRefGoogle Scholar
  13. 13.
    ISO, IEC 9126-1. (2001). ISO/IEC 9126-1:2001: Software engineering–Product Quality–Part 1: Quality model. Geneva: International Standards Organization.Google Scholar
  14. 14.
    ISO, IEC 9126-2. (2003). ISO/IEC 9126-2:2003: Software Engineering—Product Quality—Part 2: External metrics. Geneva: International Standards Organization.Google Scholar
  15. 15.
    ISO. (1986). ISO 8402 quality vocabulary. Geneva: International Organization for Standardization.Google Scholar
  16. 16.
    Jung, U. K., & Seo, D. W. (2010). An ANP approach for R&D project evaluation based on interdependencies between research objectives and evaluation criteria. Decision Support Systems, 49(3), 335–342.CrossRefGoogle Scholar
  17. 17.
    Kennedy, S., Stewart, R., Jacob, P., & Molloy, O. (2011). StoRHm: A protocol adapter for mapping SOAP based Web Services to RESTful HTTP format. Electronic Commerce Research, 11(3), 245–269.CrossRefGoogle Scholar
  18. 18.
    Kitchenham, B., & Pfleeger, S. L. (1996). Software quality: The elusive target [special issues section]. IEEE Software, 13(1), 12–21.CrossRefGoogle Scholar
  19. 19.
    Lee, J. Y., Lee, J. W., Cheun, D. W., Kim, S. D. (2009). A quality model for evaluating software-as-a-service in cloud computing. In 2009 seventh ACIS international conference on software engineering research, management and applications (pp. 261–266).Google Scholar
  20. 20.
    Marciniak, J. J. (2002). Encyclopedia of software engineering (2nd ed., Vol. 2). Chichester: Wiley.CrossRefGoogle Scholar
  21. 21.
    Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing—The business perspective. Decision Support Systems, 51(1), 176–189.CrossRefGoogle Scholar
  22. 22.
    McCall, J. A., Richards, P. K., Walters, G. F. (1977). Factors in software quality, Nat’l Tech. Information Service. Vol. 1, 2 and 3.Google Scholar
  23. 23.
    Meade, L. M., & Sarkis, J. (1999). Analyzing organizational project alternatives for agile manufacturing process. An Analytical Network Approach, International Journal of Production Research, 37, 241–261.Google Scholar
  24. 24.
    Motoei AZUMA. (2011). SquaRE The next generation of the ISO/IEC 9126 and 14598 international standards series on software product quality.
  25. 25.
    Nathan, R. J., & Yeow, P. H. P. (2011). Crucial web usability factors of 36 industries for students: A large-scale empirical study. Electronic Commerce Research, 11(2), 151–180.CrossRefGoogle Scholar
  26. 26.
    Pan, R., Guandong, X., Bin, F., Peter, D., Zhihai, W., & Martin, L. (2012). Improving recommendations by the clustering of tag neighbours. Journal of Convergence, 3(1), 13–20.Google Scholar
  27. 27.
    Panniello, U., & Gorgoglione, M. (2012). Incorporating context into recommender systems: An empirical comparison of context-based approaches. Electronic Commerce Research, 12(1), 1–30.CrossRefGoogle Scholar
  28. 28.
    Papaioannou I. V., Tsesmetzis D. T., Roussaki I. G., Anagnostou M. E. (2006). A QoS ontology language for web-services. In 20th international conference on advanced information networking and applications (Vol. 1, pp. 1–6).Google Scholar
  29. 29.
    Rachung, Y., & Tzeng, G.-H. (2006). A soft computing method for multi-criteria decision making with dependence and feedback. Applied Mathematics and Computation, 180(6), 63–75.Google Scholar
  30. 30.
    Raisinghani, M. S., Meade, L., & Schkade, L. L. (2007). Strategic e-business decision analysis using the analytic network process. IEEE Transactions on Engineering Management, 54(4), 673–686.CrossRefGoogle Scholar
  31. 31.
    Ramanathan, R. (2010). E-commerce success criteria: Determining which criteria count most. Electronic Commerce Research, 10(2), 191–208.CrossRefGoogle Scholar
  32. 32.
    Ran, S. (2003). A model for web services discovery with QoS. ACM, 4(1), 1–10.Google Scholar
  33. 33.
    Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.Google Scholar
  34. 34.
    Saaty, T. L. (2001). Decision making with dependence feedback: The analytic network process. Pittsburgh: RWS Publications.Google Scholar
  35. 35.
    Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98.CrossRefGoogle Scholar
  36. 36.
    Samadhiya, D., Wang, S.-H., Chen, D. (2010). Quality models: Role and value in software engineering. In 2010 2nd international conference on software technology and engineering (ICSTE). (Vol. 1, pp. 320–324).Google Scholar
  37. 37.
    Sibisi, M., & van Waveren, C. C. (2007). A process framework for customizing software quality models. AFRICON 2007 (pp. 1–8), 26–28.Google Scholar
  38. 38.
    Silas, S., Ezra, K., & Rajsingh, E. B. (2012). A novel fault tolerant service selection framework for pervasive computing. Human-centric Computing and Information Sciences. doi: 10.1186/2192-1962-2-5.
  39. 39.
    Subashini, S., & Kavitha, V. (2011). A survey on security issues in service delivery models of cloud computing. Journal of Network and Computer Applications, 34(1), 1–11.CrossRefGoogle Scholar
  40. 40.
    Swamynathan, G., Almeroth, K. C., & Zhao, B. Y. (2010). The design of a reliable reputation system. Electronic Commerce Research, 10(3–4), 239–270.CrossRefGoogle Scholar
  41. 41.
    Takabi, H., Joshi, J. B. D., & Ahn, G.-J. (2010). Security and privacy challenges in cloud computing environments. IEEE Security & Privacy, 8(6), 24–31.CrossRefGoogle Scholar
  42. 42.
    Tang, X., & Feng, J. (2006). ANP Theory and Application expectation. Statistics and Decision-making, 12(3), 138–140.Google Scholar
  43. 43.
    Taylor, D. G., Davis, D. F., & Jillapalli, R. (2009). Privacy concern and online personalization: The moderating effects of information control and compensation. Electronic Commerce Research, 9(3), 203–223.CrossRefGoogle Scholar
  44. 44.
    Tian, J. (2004). Quality-evaluation models and measurements. IEEE Software, 21(3), 84–91.CrossRefGoogle Scholar
  45. 45.
    Yu, T., & Lin, K. J. (2005). Service Selection Algorithms for Web Services with End-to-end QoS Constraints. Journal of Information Systems and E-Business Management, 3(2), 129–136.Google Scholar
  46. 46.
    Yuksel, I., & Dagcarondeviren, M. (2007). Using the analytic network process (ANP) in a SWOT analysis—A case study for a textile firm. Information Sciences, 177, 3364–3382.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Humanitas CollegeKyung Hee UniversitySeoulKorea

Personalised recommendations