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Identification of trustworthy cloud services: solution approaches and research directions to build an automated cloud broker

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Abstract

There are several public cloud service providers (CSPs) across the globe supplying a variety of application, platform, middleware, database, and infrastructure services. The brewing challenge before any cloud user is how to be sure about the trustworthiness of a service being offered by various CSPs. With the overwhelming usage of cloud services by individuals and organizations, this problem has acquired a lot of attention these days. Precisely arriving and articulating that this particular service from a specific cloud CSP is trustworthy is becoming complicated because of many moving variables in this whole phenomenon. There are varying parameters and indicators for decisively proving that a particular service is trustworthy or not. In order to meet up this crucial challenge and concern being widely expressed by cloud customers, researchers and cloud professionals across the world have unearthed a few viable mechanisms. There are fresh algorithms, techniques, and tools to enable cloud users towards easily and quickly selecting trustworthy services. This paper digs deeper and dwells at length about the intrinsic challenges being associated with the provenance of the trustworthiness factor. The paper also presents a machine learning approach to compute the trust factor of CSPs and it is found that the methodology gains advantage when compared to similar works.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sona College of Technology, Salem, India

    M. Marimuthu

  2. Department of Information Technology, Sona College of Technology, Salem, India

    J. Akilandeswari

  3. (SRE) Division, Reliance Jio, Bangalore, India

    P. R. Chelliah

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  1. M. Marimuthu
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  3. P. R. Chelliah
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Marimuthu, M., Akilandeswari, J. & Chelliah, P.R. Identification of trustworthy cloud services: solution approaches and research directions to build an automated cloud broker. Computing 104, 43–72 (2022). https://doi.org/10.1007/s00607-021-01015-8

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