Requirements and a proposal for the prevention of a class of service interactions in intelligent networks

Keck, Dirk O.

doi:10.1007/bfb0053499

Cited by 1 publication

(1 citation statement)

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“…Keck [132] analyses interaction emergence in today's Intelligent Networks and focuses on interactions that are caused by insufficient information and mechanisms provided by the network. In order to prevent these problems, he proposes a Session Configuration Profile (SessCoP) that is able to provide the missing (additional) information and mechanisms to the interacting services, thus giving them the opportunity to react adequately.…”

Section: Structural Preventionmentioning

confidence: 99%

The feature and service interaction problem in telecommunications systems: a survey

Keck

Kuehn

1998

IIEEE Trans. Software Eng.

153

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Today's telecommunications systems are enhanced by a large and steadily growing number of supplementary services, each of which consists of a set of service features. A situation where a combination of these services behaves differently than expected from the single services' behaviours, is called service interaction. This interaction problem is considered as a major obstacle to the introduction of new services into telecommunications networks. In this contribution, we give a survey of the work carried out in this field during the last decade. After a brief review of classification criteria that exist for feature interactions so far, we use a perspective we call the emergence level view. This perspective pays respect to the fact that the sources for interactions can be of many different kinds, like, e. g., requirement conflicts or resource contentions. It is used to rationalise the impossibility of coping with the problem with one single approach. Afterwards, we present a framework of four different criteria in order to classify the approaches dealing with the problem: The general kind of approach taken, a refinement of the well-known detection, resolution, and prevention categories, serves as the main classification criterion. It is complemented by the method used, the stage during the feature lifecycle where an approach applies, and the system (network) context. The major results of the different approaches are then presented briefly using this classification framework. We finally draw some conclusions on the applicability of this framework and on possible directions of further research in this field.

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Section: Structural Preventionmentioning

confidence: 99%