Advertisement

Soft Computing

, Volume 23, Issue 19, pp 9669–9683 | Cite as

A fuzzy-based decision-making broker for effective identification and selection of cloud infrastructure services

  • Rajganesh NagarajanEmail author
  • Ramkumar Thirunavukarasu
Methodologies and Application

Abstract

In a typical cloud environment, the task of identification and selection of required services solely depend upon the service specification furnished by the cloud user. The experienced users can specify their service requirements in precise terms by using numerical representations. On the other end, the task of service specification is always a challenging one for an inexperienced cloud user who seems to be new to the environment. In such a kind of requirement, vagueness arises in the preliminary phase of cloud service life cycle results service mismatches and affects both the cloud providers and user significantly. Hence, identifying and offering suitable services in responses to the imprecise service requirement have been emerged as an important research issue. In this paper, we have proposed a fuzzy logic-based intelligent cloud broker that clears the imprecise state of the inexperienced cloud user while furnishing the infrastructure service requirements. The proposed broker acts as an intermediate layer between the provider and consumer that finds out the appropriate services through the fuzzification and de-fuzzification process. In addition, the broker performs service aggregation through Sugeno integral and makes decision about the right services by implementing the fuzzy decision tree. The effectiveness of the broker has been validated by using MATLAB and R Studio.

Keywords

Cloud service selection Service aggregation Fuzzy logic Intelligent cloud broker Fuzzy decision tree 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal participants

This article does not contain any studies with human participants performed by any of the authors.

Supplementary material

500_2018_3534_MOESM1_ESM.pdf (843 kb)
Supplementary material 1 (pdf 843 KB)

References

  1. Anderson DT, Havens TC, Wagner C, Keller JM, Anderson MF, Wescott DJ (2014) Extension of the fuzzy integral for general fuzzy set-valued information. IEEE Trans Fuzzy Syst 22(6):1625–1639Google Scholar
  2. Ballı S, Tuker M (2017) A fuzzy multi-criteria decision analysis approach for the evaluation of the network service providers in Turkey. Intell Autom Soft Comput 1–7Google Scholar
  3. Bellman RE, Zadeh LA (1970) Decision-making in a fuzzy environment. Manag Sci 17(4):B-141MathSciNetGoogle Scholar
  4. Belohlavek R, Kruse R, Moewes C (2011) Fuzzy logic in computer science. In: Blum E, Aho A (eds) Computer science. Springer, New York, pp 385–419zbMATHGoogle Scholar
  5. Bosc P, Damiani E, Fugini M (2001) Fuzzy service selection in a distributed object-oriented environment. IEEE Trans Fuzzy Syst 9(5):682–698Google Scholar
  6. Büyüközkan G, Göçer F (2017) Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem. Appl Soft Comput 52:1222–1238Google Scholar
  7. Dastjerdi AV, Buyya R (2014) Compatibility-aware cloud service composition under fuzzy preferences of users. IEEE Trans Cloud Comput 2(1):1–13Google Scholar
  8. De Campos LM, Jorge M (1992) Characterization and comparison of Sugeno and Choquet integrals. Fuzzy Sets Syst 52(1):61–67MathSciNetzbMATHGoogle Scholar
  9. De Campos LM, Lamata MT, Moral S (1991) A unified approach to define fuzzy integrals. Fuzzy Sets Syst 39(1):75–90MathSciNetzbMATHGoogle Scholar
  10. Deng H, Yeh CH (2006) Simulation-based evaluation of defuzzification-based approaches to fuzzy multiattribute decision making. IEEE Trans Syst Man Cybern Part A Syst Hum 36(5):968–977Google Scholar
  11. Deng Y, Chan FT, Wu Y, Wang D (2011) A new linguistic mcdm method based on multiple-criterion data fusion. Expert Syst Appl 38(6):6985–6993Google Scholar
  12. Edamiani ED, Fugini M (1999) Dynamic service identification in a distributed environment. J Adv Comput Intell Intell Inf 3:401–408Google Scholar
  13. Esposito C, Ficco M, Palmieri F, Castiglione A (2016) Smart cloud storage service selection based on fuzzy logic, theory of evidence and game theory. IEEE Trans Comput 65(8):2348–2362MathSciNetzbMATHGoogle Scholar
  14. Garg SK, Versteeg S, Buyya R (2011) Smicloud: a framework for comparing and ranking cloud services. In: 2011 Fourth IEEE international conference on utility and cloud computing (UCC). IEEE, pp 210–218Google Scholar
  15. Ghosh N, Ghosh SK, Das SK (2015) Selcsp: a framework to facilitate selection of cloud service providers. IEEE Trans Cloud Comput 3(1):66–79Google Scholar
  16. Godse M, Mulik S (2009) An approach for selecting Software-as-a-Service (SaaS) product. In: IEEE international conference on cloud computing, 2009. CLOUD’09. IEEE, pp 155–158Google Scholar
  17. Grabisch M (2015) Fuzzy measures and integrals: recent developments. In: Tamir D, Rishe N, Kandel A (eds) Fifty years of fuzzy logic and its applications. Studies in fuzziness and soft computing, vol 326. Springer, Cham, pp 125–151Google Scholar
  18. Jaiswal A, Mishra R (2017) Cloud service selection using TOPSIS and fuzzy TOPSIS with AHP and ANP. In: Proceedings of the 2017 international conference on machine learning and soft computing. ACM, pp 136–142Google Scholar
  19. Janikow CZ (1998) Fuzzy decision trees: issues and methods. IEEE Trans Syst Man Cybern Part B Cybern 28(1):1–14Google Scholar
  20. Kahraman C, Cebeci U, Ulukan Z (2003) Multi-criteria supplier selection using fuzzy AHP. Logist Inf Manag 16(6):382–394Google Scholar
  21. Karim R, Ding C, Miri A (2013) An end-to-end QoS mapping approach for cloud service selection. In: 2013 IEEE Ninth World Congress on Services (SERVICES). IEEE, pp 341–348Google Scholar
  22. Krishnan AR, Kasim MM, Bakar EMNEA (2015) A short survey on the usage of Choquet integral and its associated fuzzy measure in multiple attribute analysis. Procedia Comput Sci 59:427–434Google Scholar
  23. Kumar RR, Mishra S, Kumar C (2017) Prioritizing the solution of cloud service selection using integrated MCDM methods under fuzzy environment. J Supercomput 73(11):4652–4682Google Scholar
  24. Kwon HK, Seo KK (2013) A decision-making model to choose a cloud service using fuzzy AHP. Adv Sci Technol Lett 35:93–96Google Scholar
  25. Lo CC, Chen DY, Tsai CF, Chao KM (2010) Service selection based on fuzzy topsis method. In: 2010 IEEE 24th international conference on advanced information networking and applications workshops (WAINA). IEEE, pp 367–372Google Scholar
  26. Marichal JL (2000) On Sugeno integral as an aggregation function. Fuzzy Sets Syst 114(3):347–365MathSciNetzbMATHGoogle Scholar
  27. Menzel M, Schönherr M, Tai S (2013) (\(MC^2)^2\): criteria, requirements and a software prototype for cloud infrastructure decisions. Softw Pract Exp 43(11):1283–1297Google Scholar
  28. Mesiar R, Mesiarová A (2004) Fuzzy integrals. In: International conference on modeling decisions for artificial intelligence. Springer, New York, pp 7–14Google Scholar
  29. Nagarajan R, Selvamuthukumaran S, Thirunavukarasu R (2017) A fuzzy logic based trust evaluation model for the selection of cloud services. In: 2017 International conference on computer communication and informatics (ICCCI). IEEE, pp 1–5Google Scholar
  30. Nagarajan R, Thirunavukarasu R, Shanmugam S (2018a) A cloud broker framework for infrastructure service discovery using semantic network. Int J Intell Eng Syst 11(3):11–19Google Scholar
  31. Nagarajan R, Thirunavukarasu R, Shanmugam S (2018b) A fuzzy-based intelligent cloud broker with mapreduce framework to evaluate the trust level of cloud services using customer feedback. Int J Fuzzy Syst 20(1):339–347Google Scholar
  32. Narukawa Y, Torra V (2007) Fuzzy measures and integrals in evaluation of strategies. Inf Sci 177(21):4686–4695MathSciNetzbMATHGoogle Scholar
  33. Qu C, Buyya R (2014) A cloud trust evaluation system using hierarchical fuzzy inference system for service selection. In: 2014 IEEE 28th international conference on advanced information networking and applications (AINA). IEEE, pp 850–857Google Scholar
  34. Qu L, Wang Y, Orgun MA (2013) Cloud service selection based on the aggregation of user feedback and quantitative performance assessment. In: 2013 IEEE international conference on services computing (SCC). IEEE, pp 152–159Google Scholar
  35. Rajganesh N, Ramkumar T (2016) A review on broker based cloud service model. J Comput Inf Technol 24(3):283–292Google Scholar
  36. Ramacher R, Mönch L (2015) Service selection with runtime aspects: a hierarchical approach. IEEE Trans Serv Comput 8(3):481–493Google Scholar
  37. Rao RV (2007) Decision making in the manufacturing environment: using graph theory and fuzzy multiple attribute decision making methods. Springer, New YorkzbMATHGoogle Scholar
  38. Ross TJ (2005) Fuzzy logic with engineering applications. Wiley, New YorkGoogle Scholar
  39. Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26MathSciNetzbMATHGoogle Scholar
  40. Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98MathSciNetGoogle Scholar
  41. Shivakumar U, Ravi V, Gangadharan G (2013) Ranking cloud services using fuzzy multi-attribute decision making. In: 2013 IEEE international conference on fuzzy systems (FUZZ). IEEE, pp 1–8Google Scholar
  42. Sun L, Dong H, Hussain FK, Hussain OK, Ma J, Zhang Y (2014) A hybrid fuzzy framework for cloud service selection. In: 2014 IEEE international conference on web services (ICWS). IEEE, pp 313–320Google Scholar
  43. Sun L, Ma J, Zhang Y, Dong H, Hussain FK (2016) Cloud-fuser: fuzzy ontology and mcdm based cloud service selection. Future Gener Comput Syst 57:42–55Google Scholar
  44. Tajvidi M, Ranjan R, Kolodziej J, Wang L (2014) Fuzzy cloud service selection framework. In: 2014 IEEE 3rd international conference on cloud networking (CloudNet). IEEE, pp 443–448Google Scholar
  45. Tiwari A, Sah MK, Gupta S (2015) Efficient service utilization in cloud computing exploitation victimization as revised rough set optimization service parameters. Procedia Comput Sci 70:610–617Google Scholar
  46. Tyagi S, Chambers T, Yang K (2017) Enhanced fuzzy-analytic hierarchy process. Soft Comput 22(13):4431–4443zbMATHGoogle Scholar
  47. Tzeng GH, Huang JJ (2011) Multiple attribute decision making: methods and applications. Chapman and Hall/CRC, Boca RatonzbMATHGoogle Scholar
  48. Wang TC, Lee HD (2006) Constructing a fuzzy decision tree by integrating fuzzy sets and entropy. In: Proceedings of the 5th WSEAS international conference on applied computer science. WSEAS, Stevens Point, Wisconsin, USA, pp 1547–1552Google Scholar
  49. Xu Z (2012) Intuitionistic fuzzy multiattribute decision making: an interactive method. IEEE Trans Fuzzy Syst 20(3):514–525Google Scholar
  50. Yager RR, Alajlan N (2014) Multicriteria decision-making with imprecise importance weights. IEEE Trans Fuzzy Syst 22(4):882–891Google Scholar
  51. Zi-Xiao W (1984) Fuzzy measures and measures of fuzziness. J Math Anal Appl 104(2):589–601MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Information TechnologyA.V.C. College of EngineeringMayiladuthuraiIndia
  2. 2.School of Information Technology and EngineeringVITVelloreIndia

Personalised recommendations