An autonomic architecture for optimizing QoE in multimedia access networks |
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| Authors: | Steven Latré Pieter Simoens Bart De Vleeschauwer Wim Van de Meerssche Filip De Turck Bart Dhoedt Piet Demeester Steven Van den Berghe Edith Gilon de Lumley |
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| Affiliation: | 1. Ghent University, IBBT, IBCN, Department of Information Technology, Gaston Crommenlaan 8/201, B-9050 Gent, Belgium;2. Alcatel-Lucent Bell Labs Research and Innovation, Copernicuslaan 50, B-2018 Antwerpen, Belgium;1. Institute of Communication Networks and Computer Engineering, University of Stuttgart, Pfaffenwaldring 47, 70569 Stuttgart, Germany;2. Networks Department, German University in Cairo (GUC), Egypt;1. Department of Urology, University of Pittsburgh Medical Center, Mercy Professional Building, 1350 Centre Avenue, Suite G100A, Pittsburgh, PA 15219, USA;2. Department of Urology, University of Michigan, 1500 E. Medical Center Drive, CCC 7308, Ann Arbor, MI 48109, USA;3. Department of Urology, University of Pittsburgh Medical Center, Shadyside Medical Building, 5200 Centre Avenue, Suite 209, Pittsburgh, PA 15232, USA;1. Administration and Management College, King Mongkut''s Institute of Technology Ladkrabang, Bangkok, Thailand;2. Department of Technology Management and Economics, Chalmers University of Technology, Göteborg, Sweden |
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| Abstract: | The recent emergence of multimedia services, such as Broadcast TV and Video on Demand over traditional twisted pair access networks, has complicated the network management in order to guarantee a decent Quality of Experience (QoE) for each user. The huge amount of services and the wide variety of service specifics require a QoE management on a per-user and per-service basis. This complexity can be tackled through the design of an autonomic QoE management architecture. In this article, the Knowledge Plane is presented as an autonomic layer that optimizes the QoE in multimedia access networks from the service originator to the user. It autonomously detects network problems, e.g. a congested link, bit errors on a link, etc. and determines an appropriate corrective action, e.g. switching to a lower bit rate video, adding an appropriate number of FEC packets, etc. The generic Knowledge Plane architecture is discussed, incorporating the triple design goal of an autonomic, generic and scalable architecture. The viability of an implementation using neural networks is investigated, by comparing it with a reasoner based on analytical equations. Performance results are presented of both reasoners in terms of both QoS and QoE metrics. |
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