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The Optimal Sizing of HSS Database in IMS.

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Title: The Optimal Sizing of HSS Database in IMS.
Authors: Kellovsky, Milan1, Baronak, Ivan1, Kavacky, Matej1, Chromy, Erik1
Source: Wireless Personal Communications. Oct2017, Vol. 96 Issue 4, p5453-5466. 14p.
Subjects: Next generation networks, Internet Protocol multimedia subsystem, Databases, Long-Term Evolution (Telecommunications), Markov processes
Abstract: Our paper deals with the architecture and standards of IP the multimedia subsystem from next generation networks point of view. It describes the structure of the home subscriber server database and then derives equations for sizing of its load in the proposed scenarios. We have used two methods for the database sizing-Erlang C formula and Markov chains. Finally, the achieved results and used methods are evaluated and discussed. [ABSTRACT FROM AUTHOR]
Copyright of Wireless Personal Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Our paper deals with the architecture and standards of IP the multimedia subsystem from next generation networks point of view. It describes the structure of the home subscriber server database and then derives equations for sizing of its load in the proposed scenarios. We have used two methods for the database sizing-Erlang C formula and Markov chains. Finally, the achieved results and used methods are evaluated and discussed. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Wireless Personal Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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