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Modeling of intelligent networks using SDL and an approach for feature interaction detection

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Modeling of intelligent networks using SDL and an approach for feature interaction detection

Peng, Yuan (1998) Modeling of intelligent networks using SDL and an approach for feature interaction detection. Masters thesis, Concordia University.

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Abstract

Features are novel telecommunication functions that are provided to users as individual commercial offerings. Intelligent Network (IN) is a new network framework proposed by ITU-T (International Telecommunication Union) in order to enable fast and cost-effective introduction of a large number of useful features. However, as more and more features are developed, various kinds of unexpected interference emerge among multiple features. Such interference prevents the features from fulfilling their tasks correctly. This is the problem known as Feature Interaction (FI). This thesis concentrates on the analysis and detection of Feature Interactions in Intelligent Network systems. First we present our work in modeling IN using the formal description language SDL (Specification & Description Language) based on the Distributed Functional Plane (DFP) of the Intelligent Network Conceptual Model (INCM) that is defined in the ITU-T Q.12xx series recommendations. Use of SDL in this model makes it precise, concise and free of ambiguity. This model serves as a platform to study IN entities, features and FIs. Secondly, a new detection approach is proposed in order to discover FIs efficiently. This approach attains strong detection ability and low computational complexity by focusing on scanning for major causes that lead to FIs rather than studying detailed feature behaviors. Most known FIs listed in Bellcore FI Benchmark as well as several new FIs were successfully detected using our method. Our IN model in SDL can also be used to simulate the call processing and feature running situations, which provides a potential way to verify the FI detection results of our approach, especially for new FIs.

Divisions:Concordia University > Faculty of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Peng, Yuan
Pagination:xii, 151 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:Theses (M.Comp.Sc.)
Program:Dept. of Computer Science
Date:1998
Thesis Supervisor(s):Khendek, Ferhat
ID Code:625
Deposited By:Concordia University Libraries
Deposited On:27 Aug 2009 13:13
Last Modified:08 Dec 2010 10:15
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