Automated Generation of Knowledge Plane Components for Multimedia Access Networks

Authors:
Steven Latré;Pieter Simoens;Wim Meerssche;Bart Vleeschauwer;Filip Turck;Bart Dhoedt;Piet Demeester;Steven Berghe;Edith Gilon-De Lumley
Affiliations:
IBBT - IBCN, Department of Information Technology, Ghent University,Belgium, Gent, Belgium 9050;IBBT - IBCN, Department of Information Technology, Ghent University,Belgium, Gent, Belgium 9050;IBBT - IBCN, Department of Information Technology, Ghent University,Belgium, Gent, Belgium 9050;IBBT - IBCN, Department of Information Technology, Ghent University,Belgium, Gent, Belgium 9050;IBBT - IBCN, Department of Information Technology, Ghent University,Belgium, Gent, Belgium 9050;IBBT - IBCN, Department of Information Technology, Ghent University,Belgium, Gent, Belgium 9050;IBBT - IBCN, Department of Information Technology, Ghent University,Belgium, Gent, Belgium 9050;Alcatel-Lucent Bell Labs, Antwerpen, Belgium B-2018;Alcatel-Lucent Bell Labs, Antwerpen, Belgium B-2018
Venue:
MACE '08 Proceedings of the 3rd IEEE international workshop on Modelling Autonomic Communications Environments
Year:
2008

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Abstract

The management of Quality of Experience (QoE) in the access network is largely complicated by the wide range of offered services, the myriad of possible QoE restoring actions and the increasing heterogeneity of home network configurations. The Knowledge Plane is an autonomic framework for QoE management in the access network, aiming to provide QoE management on a per user and per service basis. The Knowledge Plane contains multiple problem solving components that determine the appropriate restoring actions. Due to the wide range of possible problems and the requirement of being adaptive to new services or restoring actions, it must be possible to easily add or adapt problem solving components. Currently, generating such a problem solving component takes a lot of time and needs manual tweaking. To enable an automated generation, we present the Knowledge Plane Compiler which takes a service management objective as input, stating available monitor inputs and relevant output actions and determines a suitable neural network based Knowledge Plane incorporating this objective. The architecture of the compiler is detailed and performance results are presented.