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Security-aware multi-cloud service composition by exploiting rough sets and fuzzy FCA

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Abstract

With the emergence of cloud computing and to better satisfy users’ complex requirements in front of the huge number of cloud services, these latter may be combined while considering the virtualized environment’s constraints, including quality of services, security policies, resources availability, interoperability, etc. As composing services from multiple clouds was proved to be more beneficial than relying on services from one single cloud, the new approaches are now spanning multiple clouds. Despite the advantages of multi-cloud environments, there are always some security risks that mostly threaten the cloud consumers’ data, which makes the identification of suitable services a challenging task. In this work, we propose a security-aware multi-cloud service composition approach using fuzzy formal concept analysis (fuzzy FCA) and rough set theory (RS), which are two techniques with a strong mathematical background. To guarantee a high security level of the hosting clouds and the selected services, we exploit the fuzzy relations of fuzzy FCA and the approximation of RS. These techniques will help reducing the search space, by eliminating the disqualified clouds and insecure services. The experimental results proved the performance and the effectiveness of our approach.

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Notes

  1. https://azure.microsoft.com/en-us/services/machine-learning/.

  2. https://cloud.google.com/products/ai/.

  3. https://aws.amazon.com/machine-learning/.

  4. https://goo.gl/irhV3n.

  5. Implementation is available on this URL: https://goo.gl/c5wLos.

  6. https://sourceforge.net/projects/galicia/.

  7. US National Vulnerability Database - https://nvd.nist.gov/.

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Correspondence to Haithem Mezni.

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Author Fatma Lahmar declares that she has no conflict of interest. Author Haithem Mezni declares that he has no conflict of interest.

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Lahmar, F., Mezni, H. Security-aware multi-cloud service composition by exploiting rough sets and fuzzy FCA. Soft Comput 25, 5173–5197 (2021). https://doi.org/10.1007/s00500-020-05519-x

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