Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Soft Computing
Published in: Soft Computing 19/2019

21-09-2018 | Methodologies and Application

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

Authors: Rajganesh Nagarajan, Ramkumar Thirunavukarasu

Published in: Soft Computing | Issue 19/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article Soft concept lattice for formal concept analysis based on soft sets: theoretical foundations and applications
next article Selecting optimal blocks for image watermarking using entropy and distinct discrete firefly algorithm

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Appendix
Available only for authorised users
Literature
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):1625–1639CrossRef
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 1–7
Belohlavek R, Kruse R, Moewes C (2011) Fuzzy logic in computer science. In: Blum E, Aho A (eds) Computer science. Springer, New York, pp 385–419MATHCrossRef
Bosc P, Damiani E, Fugini M (2001) Fuzzy service selection in a distributed object-oriented environment. IEEE Trans Fuzzy Syst 9(5):682–698CrossRef
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 52:1222–1238CrossRef
Dastjerdi AV, Buyya R (2014) Compatibility-aware cloud service composition under fuzzy preferences of users. IEEE Trans Cloud Comput 2(1):1–13CrossRef
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):968–977CrossRef
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):6985–6993CrossRef
Edamiani ED, Fugini M (1999) Dynamic service identification in a distributed environment. J Adv Comput Intell Intell Inf 3:401–408CrossRef
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):2348–2362MathSciNetMATHCrossRef
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 210–218
Ghosh N, Ghosh SK, Das SK (2015) Selcsp: a framework to facilitate selection of cloud service providers. IEEE Trans Cloud Comput 3(1):66–79CrossRef
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 155–158
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 125–151
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 136–142
Janikow CZ (1998) Fuzzy decision trees: issues and methods. IEEE Trans Syst Man Cybern Part B Cybern 28(1):1–14CrossRef
Kahraman C, Cebeci U, Ulukan Z (2003) Multi-criteria supplier selection using fuzzy AHP. Logist Inf Manag 16(6):382–394CrossRef
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 341–348
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 59:427–434CrossRef
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):4652–4682CrossRef
Kwon HK, Seo KK (2013) A decision-making model to choose a cloud service using fuzzy AHP. Adv Sci Technol Lett 35:93–96
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 367–372
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):1283–1297CrossRef
Mesiar R, Mesiarová A (2004) Fuzzy integrals. In: International conference on modeling decisions for artificial intelligence. Springer, New York, pp 7–14
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 1–5
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):11–19
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):339–347CrossRef
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 850–857
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 152–159
Rajganesh N, Ramkumar T (2016) A review on broker based cloud service model. J Comput Inf Technol 24(3):283–292CrossRef
Ramacher R, Mönch L (2015) Service selection with runtime aspects: a hierarchical approach. IEEE Trans Serv Comput 8(3):481–493CrossRef
Rao RV (2007) Decision making in the manufacturing environment: using graph theory and fuzzy multiple attribute decision making methods. Springer, New YorkMATH
Ross TJ (2005) Fuzzy logic with engineering applications. Wiley, New York
Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98MathSciNet
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 1–8
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 313–320
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 57:42–55CrossRef
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 443–448
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 70:610–617CrossRef
Tyagi S, Chambers T, Yang K (2017) Enhanced fuzzy-analytic hierarchy process. Soft Comput 22(13):4431–4443MATHCrossRef
Tzeng GH, Huang JJ (2011) Multiple attribute decision making: methods and applications. Chapman and Hall/CRC, Boca RatonMATHCrossRef
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 1547–1552
Xu Z (2012) Intuitionistic fuzzy multiattribute decision making: an interactive method. IEEE Trans Fuzzy Syst 20(3):514–525CrossRef
Yager RR, Alajlan N (2014) Multicriteria decision-making with imprecise importance weights. IEEE Trans Fuzzy Syst 22(4):882–891CrossRef
Metadata
Title
A fuzzy-based decision-making broker for effective identification and selection of cloud infrastructure services
Authors
Rajganesh Nagarajan
Ramkumar Thirunavukarasu
Publication date
21-09-2018
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 19/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3534-x

Other articles of this Issue 19/2019

Soft Computing 19/2019 Go to the issue

Methodologies and Application

Dominance rule and opposition-based particle swarm optimization for two-stage assembly scheduling with time cumulated learning effect

Focus

Strength prediction of similar materials to ionic rare earth ores based on orthogonal test and back propagation neural network

Methodologies and Application

MOEA/D-GLS: a multiobjective memetic algorithm using decomposition and guided local search

Foundations

A theoretical study on the object (property) oriented concept lattices based on three-way decisions

Focus

Nucleosome positioning based on generalized relative entropy

Methodologies and Application

Furnace operation optimization with hybrid model based on mechanism and data analytics

Premium Partner

    Image Credits