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              <text>Priority based prediction mechanism for ranking providers in federated cloud architecture</text>
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          <name>Subject</name>
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              <text>Baysian network; Broker manager; Cloud broker; Grade values; Priority feedback decision tree</text>
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              <text>Cloud computing is a growing and excellent technology, as exponentially increasing the interest among users to utilize cloud applications; they need to depend on any one of the particular service provider. Now a days number of service providers also rapidly increasing in wide range, this leads ambiguity and distrust among the users. In this paper, enhanced broker based federated cloud architecture is proposed to resolve the selection of service provider issue using grading techniques and results proved that better performance improvement than single service provider selection. This broker architecture also addresses to selects the appropriate service provider automatically in the federated cloud architecture for the users submitted requests by previous experience with help of Bayesian network model. The former one implemented through concept of grade system. It is constructed for categorizing the providers based on the level of available resources. Grade and grade values distributed by applying the grade distribution algorithm for distinguishes the components. Total grade values computed for every service provider and sorted using quick sort algorithm to grade the cloud service providers. Priority based feedback decision tree technique added with this for separates similar grade cloud service provider in the selected list. Second Bayesian network model also used to rank the cloud service providers according to the previous performance of the providers with customers. Probability of satisfied customers feedback calculated for individual Service Measurements Index of Cloud Service Providers.  2018, Springer Science+Business Media, LLC, part of Springer Nature.</text>
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              <text>Saravanan M.; Aramudhan M.; SundaraPandiyan S.; Avudaiappan T.</text>
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              <text>Cluster Computing, Vol-22, pp. 9815-9823.</text>
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              <text>Springer</text>
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              <text>2019-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/s10586-017-1593-x" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s10586-017-1593-x&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040033548&amp;amp;doi=10.1007%2Fs10586-017-1593-x&amp;amp;partnerID=40&amp;amp;md5=7a55732207db70cb69f672e324918658" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040033548&amp;amp;doi=10.1007%2fs10586-017-1593-x&amp;amp;partnerID=40&amp;amp;md5=7a55732207db70cb69f672e324918658&lt;/a&gt;</text>
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              <text>ISSN: 13867857</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Saravanan M., SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India; Aramudhan M., Perunthalaivar Kamarajar College of Engineering and Tech, Karaikkal, India; SundaraPandiyan S., Christ (Deemed to be University), Bengaluru, Karnataka, India; Avudaiappan T., K. Ramakrishnan College of Technology, Trichy, Tamilnadu, India</text>
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