In this paper, we discussed the limitations of the existing approaches in cloud service ranking domain. We introduced an alternative classification of metrics used for rank- ing cloud services based on their level of fuzziness. In order to address these limita- tions, we presented an approach that allows cloud service evaluation based on a heterogeneous model of service characteristics. Based on this approach, we allowed the fuzzy expression of user preferences even for the quantitative characteristics, while we use also fuzzy numbers to model the qualitative characteristics in a more intuitive way. In addition, we used a fuzzy AHP approach that solves the problem of service ranking and allows the multi-objective assessment of cloud services in a uni- fied way (taking into account precise and imprecise metrics). Although tracing its roots in existing service ranking methods, our approach provides a more expressive and unified way to capture user opinions and preferences, both precise and imprecise. In addition, the use of linguistic terms in place of fuzzy numbers reduces the apparent complexity of the approach and makes its use more intuitive.
Moreover, we have illustrated the usage of our approach by applying it on an ex- tended version of the example in [1]. Further comparison between the proposed ap- proach and traditional approaches using solely crisp values for criteria evaluation is not considered meaningful at this stage of our research. This is because the traditional approaches make assumptions and approximations for addressing the imprecise nature of criteria while in our approach we cope with this issue by taking into account their real nature (i.e. considering their fuzziness). In addition, we provide with the means for addressing both precise and imprecise criteria in a unified way. However, in order to validate our approach we plan to conduct relevant experiments with the participa- tion of industrial users.
In our short-term future work, we plan to examine a fuzzy extension of the Analyt- ic Network Process (ANP) method in order to cope with cases where there are inter- dependent relationships among criteria. In our current approach the criteria must be independent and this is a restriction posed by AHP method. Furthermore, we plan to integrate and validate the proposed framework in a dedicated optimization mechanism that will address the need for continuous optimization in cloud service brokers. Tak- ing into account imprecise information regarding the cloud service ranking can lead to a more realistic, user-friendly and valuable solution for enhancing cloud brokerage capabilities.
Acknowledgment. The research presented in this paper is supported by the European
Union within the FP7 Marie Curie Initial Training Network “RELATE” and the FP7 ICT Broker@Cloud project.
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