iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S13198-017-0592-X
A decision-making approach based on fuzzy AHP-TOPSIS methodology for selecting the appropriate cloud solution to manage big data projects | International Journal of System Assurance Engineering and Management Skip to main content
Log in

A decision-making approach based on fuzzy AHP-TOPSIS methodology for selecting the appropriate cloud solution to manage big data projects

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

The objective of this paper is to propose a hybrid decision-making methodology based on affinity diagram, fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) to evaluate, rank and select the most appropriate cloud solutions to accommodate and manage big data projects. In fact, the strategic priority of many corporations consists in the creation of competitive advantages by using new available technologies, processes and governance mechanisms, such as big data and cloud computing. Since the technology is permanently subject to advances and developments, the question for many businesses is how to benefit from big data using the power of technical flexibility that cloud computing can provide. In this context, selecting the most adequate cloud solution to host big data projects is a complex issue that requires an extensive evaluation process. Thus, to assist users to efficiently select their most preferred cloud solution, we propose a hybrid decision-making methodology that meets these requirements in four stages. In the first stage, the identification of evaluation criteria is performed by a decision-making committee using Affinity Diagram. Due to the varied importance of the selected criteria, a FAHP process is used in the second stage to assign the importance weights for each criterion, while FTOPSIS process, in the third stage, employs these weighted criteria as inputs to evaluate and measure the performance of each alternative. In the last step, a sensitivity analysis is performed to evaluate the impact of criteria weights on the final rankings of alternatives.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

(see Hashem et al. 2015)

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Andreolini M, Colajanni M, Pietri M, Tosi S (2015) Adaptive, scalable and reliable monitoring of big data on clouds. J Parallel Distrib Comput 79–80:67–79

    Article  Google Scholar 

  • Atanassov KT (2012) On intuitionistic fuzzy sets theory. Stud Fuzziness Soft Comput. doi:10.1007/978-3-642-29127-2

    Article  MATH  Google Scholar 

  • Awasthi A, Chauhan SS (2012) A hybrid approach integrating Affinity Diagram, AHP and fuzzy TOPSIS for sustainable city logistics planning. Appl Math Model 36(2):573–584

    Article  MATH  Google Scholar 

  • Beikkhakhian Y, Javanmardi M, Karbasian M, Khayambashi B (2015) The application of ISM model in evaluating agile suppliers selection criteria and ranking suppliers using fuzzy TOPSIS-AHP methods. Expert Syst Appl 42(15):6224–6236

    Article  Google Scholar 

  • Bollier D, Firestone CM (2010) The promise and peril of big data. The Aspen Institute, Communications and Society Program, Washington

    Google Scholar 

  • Boutkhoum O, Hanine M, Tikniouine A, Agouti T (2015) Multi-criteria decisional approach of the OLAP analysis by fuzzy logic: green logistics as a case study. Arab J Sci Eng 40(8):2345–2359

    Article  Google Scholar 

  • Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets Syst 17(3):233–247

    Article  MATH  MathSciNet  Google Scholar 

  • Cavalcante E, Lopes F, Batista T, Cacho N, Delicato FC, Pires PF (2011) Cloud integrator: building value-added services on the cloud. In: 1st International symposium on network cloud computing and applications, 2011. doi:10.1109/ncca.2011.29

  • Cavalcante E, Batista T, Lopes F, Delicato FC, Pires PF, Rodriguez N, Mendes R (2012) Optimizing services selection in a cloud multiplatform scenario. In: IEEE Latin America conference on cloud computing and communications (LatinCloud), 2012. doi:10.1109/latincloud.2012.6508154

  • Chang, C-W, Liu P, Wu J-J (2012) Probability-based cloud storage providers selection algorithms with maximum availability. 41st International conference on parallel processing, 2012. doi:10.1109/icpp.2012.51

  • Chen C-T (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst 114(1):1–9

    Article  MATH  Google Scholar 

  • Chen CLP, Zhang C-Y (2014) Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf Sci 275:314–347

    Article  Google Scholar 

  • Cloud-council (2016) http://www.cloud-council.org/resource-hub.htm. Accessed 02 June 2016

  • Demirkan H, Delen D (2013) Leveraging the capabilities of service-oriented decision support systems: putting analytics and big data in cloud. Decis Support Syst 55:412–421

    Article  Google Scholar 

  • Dobre C, Xhafa F (2014) Intelligent services for big data science. Future Gener Comput Syst 37:267–281

    Article  Google Scholar 

  • Garg SK, Versteeg S, Buyya R (2011). SMICloud: a framework for comparing and ranking cloud services. In: 4th IEEE international conference on utility and cloud computing, 2011. doi:10.1109/ucc.2011.36

  • Gil-Lafuente AM, Merigó JM, Vizuete E (2014) Analysis of luxury resort hotels by using the fuzzy analytic hierarchy process and the fuzzy Delphi method. Econ Res-Ekonomska Istraživanja 27(1):244–266

    Article  Google Scholar 

  • Gul M, Guneri AF (2016) A fuzzy multi criteria risk assessment based on decision matrix technique: a case study for aluminum industry. J Loss Prev Process Ind 40:89–100

    Article  Google Scholar 

  • Gumus AT (2009) Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology. Expert Syst Appl 36:4067–4074

    Article  Google Scholar 

  • Hanine M, Boutkhoum O, Tikniouine A, Agouti T (2016) A new web-based framework development for fuzzy multi-criteria group decision-making. SpringerPlus 5(1):601. doi:10.1186/s40064-016-2198-1

    Article  Google Scholar 

  • Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU (2015) The rise of big data on cloud computing: review and open research issues. Inf Syst 47:98–115

    Article  Google Scholar 

  • He Q, Han J, Yang Y, Grundy J, Jin H (2012) QoS-driven service selection for multi-tenant SaaS. In: IEEE 5th international conference on cloud computing, 2012. doi:10.1109/cloud.2012.125

  • House W (2014). Big data and privacy: a technological perspective. Washington, DC: Executive Office of the President, President’s Council of Advisors on Science and Technology

  • Hussain FK, Hussain OK (2011) Towards multi-criteria cloud service selection. In: 5th International conference on innovative mobile and internet services in ubiquitous computing, 2011. doi:10.1109/imis.2011.99

  • Hwang C-L, Yoon K (1981) Multiple attribute decision making methods and applications. In: A state-of-art survey. Springer, Berlin

  • Ic Y (2012) An experimental design approach using TOPSIS method for the selection of computer-integrated manufacturing technologies. Robot Comput-Integr Manuf 28(2):245–256

    Article  Google Scholar 

  • Junior FRL, Carpinetti LCR (2016) Combining SCOR® model and fuzzy TOPSIS for supplier evaluation and management. Int J Prod Econ 174:128–141

    Article  Google Scholar 

  • Junior FRL, Osiro L, Carpinetti LCR (2014a) A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Appl Soft Comput 21:194–209

    Article  Google Scholar 

  • Junior FRL, Osiro L, Carpinetti LCR (2014b) A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Appl Soft Comput 21:194–209

    Article  Google Scholar 

  • Kannan G, Pokharel S, Sasi Kumar P (2009) A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resour Conserv Recycl 54(1):28–36

    Article  Google Scholar 

  • Karami A, Johansson R (2014) Utilization of multi attribute decision making techniques to integrate automatic and manual ranking of options. J Inf Sci Eng 30:519–534

    Google Scholar 

  • Karim R, Ding C, Miri A (2013) An End-to-End QoS mapping approach for cloud service selection. In: IEEE 9th world congress on services, 2013. doi:10.1109/services.2013.71

  • Kilic HS, Zaim S, Delen D (2014) Development of a hybrid methodology for ERP system selection: the case of Turkish Airlines. Decis Support Syst 66:82–92

    Article  Google Scholar 

  • Kilic HS, Zaim S, Delen D (2015) Selecting “The Best” ERP system for SMEs using a combination of ANP and PROMETHEE methods. Expert Syst Appl 42:2343–2352

    Article  Google Scholar 

  • Kusumawardani RP, Agintiara M (2015) Application of fuzzy AHP-TOPSIS method for decision making in human resource manager selection process. Proc Comput Sci 72:638–646

    Article  Google Scholar 

  • Liang F, Lu X (2015) Accelerating iterative big data computing through MPI. J Comput Sci Technol 30(2):283–294

    Article  MathSciNet  Google Scholar 

  • Limam N, Boutaba R (2010) Assessing software service quality and trustworthiness at selection time. IIEEE Trans Softw Eng 36(4):559–574. doi:10.1109/tse.2010.2

    Article  Google Scholar 

  • Lynch C (2008) Big data: how do your data grow? Nature 455:28–29

    Article  Google Scholar 

  • Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH (2011) Big data: The next frontier for innovation, competition, and productivity. Report McKinsey Global Institute, May 2011

  • Martens B, Teuteberg F (2011) Decision-making in cloud computing environments: a cost and risk based approach. Inf Syst Front 14(4):871–893. doi:10.1007/s10796-011-9317-x

    Article  Google Scholar 

  • Menzel M, Schönherr M, Tai S (2011) (MC2)2: criteria, requirements and a software prototype for Cloud infrastructure decisions. Softw: Pract Exp 43(11):1283–1297. doi:10.1002/spe.1110

    Google Scholar 

  • Miller HE (2013) Big-data in cloud computing: a taxonomy of risks. Inf Res 18(1):571

    Google Scholar 

  • Mosadeghi R, Warnken J, Tomlinson R, Mirfenderesk H (2015) Comparison of Fuzzy-AHP and AHP in a spatial multi-criteria decision making model for urban land-use planning, computers. Environ Urban Syst 49:54–65

    Article  Google Scholar 

  • Mousavi SM, Tavakkoli-Moghaddam R, Heydar M, Ebrahimnejad S (2013) Multi-criteria decision making for plant location selection: an integrated delphi-AHP-PROMETHEE methodology. Arab J Sci Eng 38(5):1255–1268

    Article  Google Scholar 

  • Nie G, She Q, Chen D (2011). Evaluation index system of cloud service and the purchase decision- making process based on AHP. In: Proceedings of the 2011 international conference on informatics, cybernetics, and computer engineering (ICCE2011) Nov 19–20 2011, Melbourne, Australia, pp 345–352. doi:10.1007/978-3-642-25194-8_42

  • Nizamani S, Dew P, Djemame K (2013) A quality-aware cloud management service for computational modellers. IJCC 2(4):340. doi:10.1504/ijcc.2013.058097

    Article  Google Scholar 

  • Noori B (2014) Strategic business unit ranking based on innovation performance: a case study of a steel manufacturing company. Int J Syst Assur Eng Manag. doi:10.1007/s13198-014-0283-9

    Google Scholar 

  • Onar SC, Oztaysi B, Kahraman C (2014) Strategic decision selection using hesitant fuzzy TOPSIS and interval type-2 fuzzy AHP: a case study. Int J Comput Intell Syst 7(5):1002–1021

    Article  Google Scholar 

  • Palmieri F, Fiore U, Ricciardi S, Castiglione A (2016) GRASP-based resource re-optimization for effective big data access in federated clouds. Future Gener Comput Syst 54:168–179. doi:10.1016/j.future.2015.01.017

    Article  Google Scholar 

  • Patil SK, Kant R (2014) A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers. Expert Syst Appl 41:679–693

    Article  Google Scholar 

  • Peng A-H, Xiao X-M (2013) Material selection using PROMETHEE combined with analytic network process under hybrid environment. Mater Des 47:643–652

    Article  Google Scholar 

  • Prakash C, Barua MK (2015) Integration of AHP-TOPSIS method for prioritizing the solutions of reverse logistics adoption to overcome its barriers under fuzzy environment. J Manuf Syst 37:599–615

    Article  Google Scholar 

  • Purcell BM (2013) Big data using cloud computing. J Technol Res 5:1–8

    Google Scholar 

  • Qu L, Wang Y, Orgun MA, Liu L, Bouguettaya A (2014) Context-aware cloud service selection based on comparison and aggregation of user subjective assessment and objective performance assessment. In: IEEE international conference on web services, 2014. doi:10.1109/icws.2014.24

  • Rawal B, Liang S, Tsetse A, Ramcharan H (2014) Split-encoding: the next frontier tool for big data. Adv Comput Netw Inf 1:501–510. doi:10.1007/978-3-319-07353-8_58

    Google Scholar 

  • Renu RS, Mocko G, Koneru A (2013) Use of big data and knowledge discovery to create data backbones for decision support systems. Proc Comput Sci 20:446–453

    Article  Google Scholar 

  • Ruiz-Alvarez A, Humphrey M (2011) An automated approach to cloud storage service selection. In: Proceedings of the 2nd international workshop on scientific cloud computing—sciencecloud’11. doi:10.1145/1996109.1996117

  • Saaty T (1980) The analytic hierarchy process. McGraw-Hill, New York

    MATH  Google Scholar 

  • Sadiq M, Jain SK (2014) Applying fuzzy preference relation for requirements prioritization in goal oriented requirements elicitation process. Int J Syst Assur Eng Manag 5(4):711–723

    Article  Google Scholar 

  • Salama M, Shawish A, Zeid A, Kouta M (2012) Integrated QoS utility-based model for cloud computing service provider selection. In: IEEE 36th annual computer software and applications conference workshops, 2012. doi:10.1109/compsacw.2012.18

  • Saripalli P, Pingali G (2011) MADMAC: multiple attribute decision methodology for adoption of clouds. In: IEEE 4th international conference on cloud computing, 2011. doi:10.1109/cloud.2011.61

  • Silas S, Rajsingh EB, Ezra K (2012) Efficient service selection middleware using ELECTRE methodology for cloud environments. Inf Technol J 11(7):868–875. doi:10.3923/itj.2012.868.875

    Article  Google Scholar 

  • Singh N, Tyagi K (2015) Ranking of services for reliability estimation of SOA system using fuzzy multicriteria analysis with similarity-based approach. Int J Syst Assur Eng Manag. doi:10.1007/s13198-015-0339-5

    Google Scholar 

  • Smowton C, Balla A, Antoniades D, Miller C, Pallis G, Dikaiakos MD, Xing W (2015) A cost-effective approach to improving performance of big genomic data analyses in clouds. Future Gener Comput Syst. doi:10.1016/j.future.2015.11.011

    Google Scholar 

  • Snijders C, Matzat U, Reips UD (2012) Big data: big gaps of knowledge in the field of Internet science. Int J Internet Sci 7(1):1–5

    Google Scholar 

  • Sookhak M, Gani A, Khan MK, Buyya R (2015) Dynamic remote data auditing for securing big data storage in cloud computing. Inf Sci. doi:10.1016/j.ins.2015.09.004

    Google Scholar 

  • Sundareswaran S, Squicciarini A, Lin D (2012) A Brokerage-based approach for cloud service selection. In: IEEE 5th international conference on cloud computing, 2012. doi:10.1109/cloud.2012.119

  • Szalay A, Gray J (2006) Science in an exponential world. Nature 440:413–414

    Article  Google Scholar 

  • Talia D (2013) Clouds for scalable big data analytics. Computer 46:98–101

    Article  Google Scholar 

  • Thomas Foster S (2010) Managing quality: integrating the supply chain, 4th edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Tsao C-T, Chu C-T (2001) Personnel selection using an improved fuzzy MCDM algorithm. J Inf Optim Sci 22(3):521–536

    MATH  Google Scholar 

  • Tsuchiya S, Sakamoto Y, Tsuchimoto Y, Lee V (2012) Big Data Processing in Cloud Environments. FUJITSU Sci Technol J 48(2):159–168

    Google Scholar 

  • Tyagi M, Kumar P, Kumar D (2015) Parametric selection of alternatives to improve performance of green supply chain management system. Proc—Soc Behav Sci 189:449–457

    Article  Google Scholar 

  • Uygun Ö, Dede A (2016) Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques. Comput Ind Eng. doi:10.1016/j.cie.2016.02.020

    Google Scholar 

  • Wang X, Cao J, Xiang Y (2015) Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing. J Syst Softw 100:195–210

    Article  Google Scholar 

  • Weichselbraun A, Gindl S, Scharl A (2014) Enriching semantic knowledge bases for opinion mining in big data applications. Knowl-Based Syst 69:78–85

    Article  Google Scholar 

  • Whaiduzzaman M, Gani A, Anuar NB, Shiraz M, Haque MN, Haque IT (2014) Cloud service selection using multicriteria decision analysis. Sci World J. doi:10.1155/2014/459375

    Google Scholar 

  • Yan D, Yin XS, Lian C, Zhong X, Zhou X, Wu GS (2015) Using memory in the right way to accelerate big data processing. J Comput Sci Technol 30(1):30–41

    Article  Google Scholar 

  • Yang C-C, Chen B-S (2004) Key quality performance evaluation using fuzzy AHP. J Chin Inst Ind Eng 21:543–550

    Google Scholar 

  • Yang J, Lin W, Dou W (2013) An adaptive service selection method for cross-cloud service composition. Concurr Comput: Pract Exp 25(18):2435–2454. doi:10.1002/cpe.3080

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

  • Zardari NH, Ahmed K, Shirazi SM, Yusop ZB (2015) Weighting methods and their effects on multi-criteria decision making model outcomes in water resources management. SpringerBriefs Water Sci Technol. doi:10.1007/978-3-319-12586-2

    Article  Google Scholar 

  • Zare K, Mehri-Tekmeh J, Karimi S (2015) A SWOT framework for analyzing the electricity supply chain using an integrated AHP methodology combined with fuzzy-TOPSIS. Int Strateg Manag Rev 3(1):66–80

    Article  Google Scholar 

  • Zeng W, Zhao Y, Zeng J (2009) Cloud service and service selection algorithm research. In: Proceedings of the 1st ACM/SIGEVO summit on genetic and evolutionary computation—GEC’09. doi:10.1145/1543834.1544004

  • Zhang L, Wu C, Li Z, Guo C, Chen M, Lau FCM (2013) Moving big data to the cloud: an online cost-minimizing approach. IEEE J Sel Areas Commun 31:2710–2721

    Article  Google Scholar 

  • 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(3):962–973. doi:10.1016/j.jnca.2011.03.013

    Article  Google Scholar 

  • Zhu G-N, Hu J, Qi J, Gu C-C, Peng Y-H (2015) An integrated AHP and VIKOR for design concept evaluation based on rough number. Adv Eng Inform. doi:10.1016/j.aei.2015.01.010

    Google Scholar 

  • Zyoud SH, Kaufmann LG, Shaheen H, Samhan S, Fuchs-Hanusch D (2016) A framework for water loss management in developing countries under fuzzy environment: integration of Fuzzy AHP with Fuzzy TOPSIS. Expert Syst Appl 61:86–105

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to thank Mr. R. Boulguid for pointing out many English corrections that lead to the improvement of the paper. They also like to thank the reviewers for their remarks and valuable suggestions offered.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Omar Boutkhoum.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (XLSX 60 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boutkhoum, O., Hanine, M., Agouti, T. et al. A decision-making approach based on fuzzy AHP-TOPSIS methodology for selecting the appropriate cloud solution to manage big data projects. Int J Syst Assur Eng Manag 8 (Suppl 2), 1237–1253 (2017). https://doi.org/10.1007/s13198-017-0592-x

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-017-0592-x

Keywords

Navigation