The Journal of Supercomputing

, Volume 66, Issue 2, pp 614–632 | Cite as

The QoS-based MCDM system for SaaS ERP applications with Social Network

Article

Abstract

Cloud computing delivers almost all of its services including software, user’s data, system resources, processes and their computation over the Internet. Cloud computing consists of three main classes; Software as a Service, Infrastructure as a Service and Platform as a Service. Using Software as a Service (SaaS), users are able to rent application software and databases which they then install onto their computer in the traditional way. In the Enterprise Resource Planning (ERP) system, the system service environment changed so as to allow the application of the SaaS in the cloud computing environment. This change was implemented in order to provide the ERP system service to users in a cheaper, more convenient and efficient form through the Internet as opposed to having to set up their own computer. Recently many SaaS ERP packages are available on the Internet. For this reason, it is very difficult for users to find the SaaS ERP package that would best suit their requirements. The QoS (Quality of Service) model can provide a solution to this problem. However, according to recent research, not only quality attributes’ identification for SaaS ERP, but also a process for finding and recommending software in the cloud computing environment, has proved to be lacking. In this paper, we propose a QoS model for SaaS ERP. The proposed QoS model consists of 6 criteria; Functionality, Reliability, Usability, Efficiency, Maintainability and Business. Using this QoS model, we propose a Multi Criteria Decision Making (MCDM) system that finds the best fit for the SaaS ERP in the cloud computing environment and makes recommendations to users in priority order. In order to organize the quality clusters, we organized an expert group and got their opinion to organize the quality clusters using Social Network Group. Social Networks can be used efficiently to get opinion by various types of expert groups. In order to establish the priority, we used pairwise comparisons to calculate the priority weights of each quality attribute while accounting for their interrelation. Finally, using the quality network model and priority weights, this study evaluated three types of SaaS ERPs. Our results show how to find the most suitable SaaS ERPs according to their correlation with the criteria and to recommend a SaaS ERP package which best suits users’ needs.

Keywords

SaaS SaaS ERP Cloud computing SaaS quality MCDM Quality evaluation model Pairwise comparison 

References

  1. 1.
    Aramand M (2008) Software products and services are high tech? New product development strategy for software products and services. Technovation 28:154–160 CrossRefGoogle Scholar
  2. 2.
    Baek S-J, Park S-M, Yang S-H, Song E-H, Jeong Y-S (2010) Efficient server virtualization using grid service infrastructure. J Inf Process Syst 6(4). doi:10.3745/JIPS.2010.6.4.553
  3. 3.
    Benlian A, Hess T (2011) Opportunities and risks of software-as-a-service: findings from a survey of IT executives. Decis Support Syst 52:232–246 CrossRefGoogle Scholar
  4. 4.
    Bhattacharya A, Wu W, Yang Z (2012) Quality of experience evaluation of voice communication: an affect-based approach. Hum-Centric Comput Inf Sci 2(7). doi:10.1186/2192-1962-2-7
  5. 5.
    Botta-Genoulaz V, Millet P-A (2005) A classification for better use of ERP systems. Comput Ind 56:573–587 CrossRefGoogle Scholar
  6. 6.
    Bozoki S, Fulop J, Ronyai L (2010) On optimal completion of incomplete pairwise comparison matrices. Math Comput Model 52:318–333 MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Buter B, Dijkshoorn N, Modolo D, Nguyen Q, van Noort S, van de Poel B, Salah AA, Salah AAA (2011) Explorative visualization and analysis of a Social Network for arts: the case of deviant ART. J Converg 2(1):87–94 Google Scholar
  8. 8.
    Chadwick DW, Fatema K (2012) A privacy preserving authorisation system for the cloud. J Comput Syst Sci. doi:10.1016/j.jcss.2011.12.019 Google Scholar
  9. 9.
    Challa JS, Paul A, Dada Y, Nerella V, Srivastava PR, Singh AP (2011) Integrated software quality evaluation: a fuzzy multi-criteria approach. J Inf Process Syst 7(3):473–518 Google Scholar
  10. 10.
    Cuéllar MP, Delgado M, Pegalajar MC (2011) Improving learning management through semantic web and social networks in e-learning environments. Expert Syst Appl 38:4181–4189 CrossRefGoogle Scholar
  11. 11.
    DeLone WH, McLean ER (2003) The DeLone and McLean model of information systems success: a ten-year update. J Manag Inf Syst 19(4):9–30 Google Scholar
  12. 12.
    Deng Y, Chan FTS, Wu Y, Wang D (2011) A new linguistic MCDM method based on multiple-criterion data fusion. Expert Syst Appl 38:6985–6993 CrossRefGoogle Scholar
  13. 13.
    Fan M, Kumar S, Whinston AB (2009) Short-term and long-term competition between providers of shrink-wrap software and software as a service. Eur J Oper Res 196:661–671 MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Garg SK, Versteeg S, Buyya R (2012) A framework for ranking of cloud computing services. Future Gener Comput Syst. doi:10.1016/j.future.2012.06.006 Google Scholar
  15. 15.
    James A, Dimitrijev S (2012) Ranked selection of nearest discriminating features. Hum-Centric Comput Inf Sci 2(12). doi:10.1186/2192-1962-2-12
  16. 16.
    Jeong HY, Kim YH (2012) A system software quality model using DeLone & McLean model and ISO/IEC 9126. Int J Digit Content Technol Appl 6(5):181–188 MathSciNetCrossRefGoogle Scholar
  17. 17.
    Jeong D, Shin H, Baik D-K, Jeong Y-S (2009) An efficient web ontology storage considering hierarchical knowledge for Jena-based applications. J Inf Process Syst 5(1):11–18 Google Scholar
  18. 18.
    Jin H, Xiang G, Zou D, Wu S, Zhao F, Li M, Zheng W (2011) A VMM-based intrusion prevention system in cloud computing environment. J Supercomput. doi:10.1007/s11227-011-0608-2 Google Scholar
  19. 19.
    Jun L, Jun W (2011) Cloud computing based solution to decision making. Proc Eng 15:1822–1826 CrossRefGoogle Scholar
  20. 20.
    Katsaros G, Kousiouris G, Gogouvitis SV, Kyriazis D, Menychtas A, Varvarigou T (2011) A self-adaptive hierarchical monitoring mechanism for clouds. J Syst Softw. doi:10.1016/j.jss.2011.11.1043 Google Scholar
  21. 21.
    Kim H-N, El Saddik A, Jung J-G (2012) Leveraging personal photos to inferring friendships in social network services. Expert Syst Appl 39:6955–6966 CrossRefGoogle Scholar
  22. 22.
    Kima W, Lee JH, Hong C, Han C, Lee H, Jang B (2012) An innovative method for data and software integration in SaaS. Comput Math Appl. doi:10.1016/j.camwa.2012.03.069 Google Scholar
  23. 23.
    Klyuev V, Yokoyama A (2010) Web query expansion: a strategy utilising Japanese WordNet. J Converg 1(1) Google Scholar
  24. 24.
    Kumar V, Maheshwari B, Kumar U (2003) An investigation of critical management issues in ERP implementation: empirical evidence from Canadian organizations. Technovation 23:793–807 CrossRefGoogle Scholar
  25. 25.
    Limayem F, Yannou B (2007) Selective assessment of judgmental inconsistencies in pairwise comparisons for group decision rating. Comput Oper Res 34:1824–1841 CrossRefMATHGoogle Scholar
  26. 26.
    Liu D, Stewart TJ (2004) Integrated object-oriented framework for MCDM and DSS modeling. Decis Support Syst 38:421–434 CrossRefGoogle Scholar
  27. 27.
    Luo H, Shyu M-L (2011) Quality of service provision in mobile multimedia—a survey. Hum-Centric Comput Inf Sci 1(6):1–15 Google Scholar
  28. 28.
    Malhotra R, Temponi C (2010) Critical decisions for ERP integration: small business issues. Int J Inf Manag 30:28–37 CrossRefGoogle Scholar
  29. 29.
    Manufacturing SaaS ERP system for cloud computing (2012) http://www.plex.com/
  30. 30.
    Michael A (2010) Elliott, selecting numerical scales for pairwise comparisons. Reliab Eng Syst Saf 95:750–763 CrossRefGoogle Scholar
  31. 31.
    Oommen BJ, Yazidi A, Granmo O-C (2012) An adaptive approach to learning the preferences of users in a Social Network using weak estimators. J Inf Process Syst 8(2):191–212 Google Scholar
  32. 32.
    Pan R, Xu G, Fu B, Dolog P, Wang Z, Leginus M (2012) Improving recommendations by the clustering of tag neighbours. J Converg 3(1):13–20 Google Scholar
  33. 33.
    Pan R, Xu G, Fu B, Dolog P, Wang Z, Leginus M (2012) Improving recommendations by the clustering of tag neighbours. J Converg 3(1):13–20 Google Scholar
  34. 34.
    Qin XS, Huang GH, Chakma A, Nie XH, Lin QG (2008) A MCDM-based expert system for climate-change impact assessment and adaptation planning? A case study for the Georgia Basin, Canada. Expert Syst Appl 34:2164–2179 CrossRefGoogle Scholar
  35. 35.
    Rouse M (2012) Definition of SaaS ERP (software-as-a-service ERP hosting). http://searchmanufacturingerp.techtarget.com/definition/SaaS-ERP
  36. 36.
    Saaty TL (2006) Fundamentals of decision making and priority theory with the analytic hierarchy process. RWS, Pittsburgh Google Scholar
  37. 37.
    Salmeron JL, Lopez C (2010) A multicriteria approach for risks assessment in ERP maintenance. J Syst Softw 83:1941–1953 Google Scholar
  38. 38.
    Siraj S, Mikhailov L, Keane JA (2012) Enumerating all spanning trees for pairwise comparisons. Comput Oper Res 39:191–199 MathSciNetCrossRefMATHGoogle Scholar
  39. 39.
    Sultan NA (2010) Reaching for the cloud: how SMEs can manage. Int J Inf Manag. doi:10.1016/j.ijinfomgt.2010.08.001 Google Scholar
  40. 40.
    Sung K, Kong H-K, Kim T (2011) Convergence indicator: the case of cloud computing. J Supercomput. doi:10.1007/s11227-011-0706-1 Google Scholar
  41. 41.
    Verville J, Halingten A (2003) A six-stage model of the buying process for ERP software. Ind Mark Manage 32:585–594 CrossRefGoogle Scholar
  42. 42.
    Wei G, Vasilakos AV, Zheng Y, Xiong N (2010) A game-theoretic method of fair resource allocation for cloud computing services. J Supercomput 54:252–269. doi:10.1007/s11227-009-0318-1 CrossRefGoogle Scholar
  43. 43.
    Wu Z, Liu X, Ni Z, Yuan D, Yang Y (2011) A market-oriented hierarchical scheduling strategy in cloud workflow systems. J Supercomput. doi:10.1007/s11227-011-0578-4 Google Scholar
  44. 44.
    Xie S, Sun Z (2011) Improve navigation software quality by using prediction model of coding stage defect density. J Converg Inf Technol 6(4):64–69 CrossRefGoogle Scholar
  45. 45.
    Xu X (2012) From cloud computing to cloud manufacturing. Robot Comput-Integr Manuf 28:75–86 CrossRefGoogle Scholar
  46. 46.
    Yu H (2012) An approach to evaluating the software quality based on intelligent computation with intuitionistic fuzzy information. Int J Adv Comput Technol 4(4):276–282 CrossRefGoogle Scholar
  47. 47.
    Zhang Z, Lee MKO, Huang P, Zhang L, Huang X (2005) A framework of ERP systems implementation success in China: an empirical study. Int J Prod Econ 98:56–80 CrossRefGoogle Scholar
  48. 48.
    Zhao L, Ren Y, Li M, Sakurai K (2012) Flexible service selection with user-specific QoS support in service-oriented architecture. J Netw Comput Appl 35:962–973 CrossRefGoogle Scholar
  49. 49.
    Zimbres RA (2009) Dynamics of quality perception in a Social Network: a cellular automaton based model in aesthetics services. Electron Notes Theor Comput Sci 252:157–180 MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSeoul National University of Science and TechnologySeoulRepublic of Korea
  2. 2.Humanitas CollegeKyung Hee UniversitySeoulRepublic of Korea

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