Abstract
The increase of cloud technology solutions has made the evaluation and selection of desired cloud services, a cumbersome task for the user. In particular, the lack of standard mechanisms that allow the comparison of cloud service specifications against user requirements taking into account the implicit uncertainty and vagueness is a major hindrance during the cloud service evaluation and selection. In this paper, we discuss an alternative classification of metrics used for ranking cloud services based on their level of fuzziness and present an approach that allows cloud service evaluation based on a heterogeneous model of service characteristics. Our approach allows the multi-objective assessment of cloud services in a unified way, taking into account precise and imprecise metrics. We use fuzzy numbers to model the imprecise service characteristics and vague user preferences and we validate a fuzzy AHP approach that solves the problem of service ranking.
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References
Garg, S.K., Versteeg, S., Buyya, R.: SMICloud: A Framework for Comparing and Ranking Cloud Services. Presented at the Fourth IEEE International Conference on Utility and Cloud Computing, Victoria, NSW, pp. 210–218 (2011), doi:10.1109/UCC.2011.36
Godse, M., Mulik, S.: An Approach for Selecting Software-as-a-Service (SaaS) Product. In: 2009 IEEE International Conference on Cloud Computing (2009)
Cloud Service Measurement Index Consortium (CSMIC) (n.d.). SMI Framework. Introducing the Service Measurement Index, http://www.cloudcommons.com/web/cc/SMIintro (retrieved)
Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)
Ross, T.J.: Fuzzy Logic with Engineering Applications, 3rd edn. John Wiley & Sons (2010)
Buckley, J.J.: Ranking alternatives using fuzzy numbers. Fuzzy Sets Systems 15(1), 21–31 (1985)
Kwong, C.K., Bai, H.: A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment. Journal of Intelligent Manufacturing 13(5), 367–377 (2002), doi:10.1023/A:1019984626631
Chan, K.Y., Dillon, T.S., Kwong, C.K.: An Enhanced Fuzzy AHP Method with Extent Analysis for Determining Importance of Customer Requirements. In: Chan, K.Y., Kwong, C.K., Dillon, T.S. (eds.) Comput. Intell. Techniques for New Product Design. SCI, vol. 403, pp. 79–94. Springer, Heidelberg (2012)
Chang, D.-Y.: Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research 95(3), 649–655 (1996), doi:dx.doi.org/10.1016/0377-2217(95)00300-2
Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill International (1980)
Durán, O., Aguilo, J.: Computer-aided machine-tool selection based on a Fuzzy-AHP approach. Expert Systems with Applications 34(3), 1787–1794 (2008), doi:dx.doi.org/10.1016/j.eswa.2007.01.046
Han, S.-M., Hassan, M.M., Yoon, C.-W., Huh, E.-N.: Efficient service recommendation system for cloud computing market. In: 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human (2009)
Pawluk, P., Simmons, B., Smit, M., Litoiu, M., Mankovski, S.: Introducing STRATOS: A Cloud Broker Service. In: 5th IEEE International Conference on Cloud Computing (CLOUD), pp. 891–898 (2012)
Almulla, M., Almatori, K., Yahyaoui, H.: A QoS-based Fuzzy Model for Ranking Real WorldWeb Services. Presented at the IEEE International Conference on Web Services (2011)
Benouaret, K., Benslimane, D., Hadjali, A., Barhamgi, M.: Top-k Web Service Compositions using Fuzzy Dominance Relationship. Presented at the IEEE International Conference on Services Computing (2011)
Chao, K.-M., Younas, M., Lo, C.-C., Tan, T.-H.: Fuzzy Matchmaking for Web Services. Presented at the 19th International Conference on Advanced Information Networking and Applications, AINA 2005 (2005)
Huang, C.-L., Chao, K.-M., Lo, C.-C.: A Moderated Fuzzy Matchmaking for Web Services. Presented at the the Fifth International Conference on Computer and Information Technology, CIT 2005 (2005)
Lin, M., Xie, J., Guo, H., Wang, H.: Solving QoS-driven Web Service Dynamic Composition as Fuzzy Constraint Satisfaction. Presented at the IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE 2005). (2005)
Lin, W.-L., Lo, C.-C., Chao, K.-M., Younas, M.: Fuzzy Consensus on QoS in Web Services Discovery. Presented at the 20th International Conference on Advanced Information Networking and Applications, AINA 2006 (2006)
Liu, X(F.), Fletcher, K.K., Tang, M.: Service Selection based on Perso-nalized Preference and Trade-Offs among QoS. Presented at the IEEE First International Conference on Service Economics (2012)
Nepal, S., Sherchan, W., Hunklinger, J., Bouguettaya, A.: A Fuzzy Trust Management Framework for Service Web. Presented at the IEEE International Conference on Web Services (2010)
Meixner, O.: Fuzzy AHP Group Decision Analysis and its Application for the Evaluation of Energy Sources. Presented at the 10th International Symposium on the Analytic Hierarchy/Network Process Multicriteria Decision Making, Pittsburgh, Penn-sylvania, USA (2009)
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Patiniotakis, I., Rizou, S., Verginadis, Y., Mentzas, G. (2013). Managing Imprecise Criteria in Cloud Service Ranking with a Fuzzy Multi-criteria Decision Making Method. In: Lau, KK., Lamersdorf, W., Pimentel, E. (eds) Service-Oriented and Cloud Computing. ESOCC 2013. Lecture Notes in Computer Science, vol 8135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40651-5_4
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DOI: https://doi.org/10.1007/978-3-642-40651-5_4
Publisher Name: Springer, Berlin, Heidelberg
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