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.
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References
Latré, S., et al.: Design of a generic knowledge base for autonomic QoE optimization in multimedia access networks. In: ACNM 2008: 2nd IEEE Workshop on Autonomic Communications for Network Management,
Clark, D.D., et al.: A knowledge plane for the internet. In: SIGCOMM 2003: Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications. ACM, New York (2003)
Urra, A., et al.: Adding new components to the knowledge plane GMPLS over WDM networks. In: IEEE Workshop on IP Operations and Management, 2004, October 11-13 (2004)
Teixeira, R., et al.: A measurement framework for pin-pointing routing changes. In: NetT 2004: Proceedings of the ACM SIGCOMM workshop on Network troubleshooting, pp. 313–318 (2004)
De Vleeschauwer, B., et al.: On the Enhancement of QoE for IPTV Services through Knowledge Plane Deployment. In: Broadband Europe (2006)
Simoens, P., et al.: Design of an autonomic QoE reasoner for improving access network performance. In: Fourth International Conference on Autonomic and Autonomous Systems, 2008. ICAS 2008 (2008)
Sutton, R., et al.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Ouferhat, N., et al.: A qos scheduler packets for wireless sensor networks. In: IEEE/ACS International Conference on Computer Systems and Applications, 2007. AICCSA 2007, May 13-16 (2007)
Niyato, D., et al.: Radio resource management in mimo-ofdm- mesh networks: Issues and approaches. IEEE Communications Magazine (2007)
Vengerov, D.: A reinforcement learning approach to dynamic resource allocation. Engineering Applications of Artificial Intelligence 20(3), 383–390 (2007)
Tesauro, G., et al.: On the use of hybrid reinforcement learning for autonomic resource allocation. Cluster Computing 10(3), 287–299 (2007)
Vienne, P., et al.: A middleware for autonomic QoS management based on learning. In: SEM 2005: Proceedings of the 5th international workshop on Software engineering and middleware, pp. 1–8. ACM, New York (2005)
Bahati, R.M., et al.: Adapting to run-time changes in policies driving autonomic management. In: Fourth International Conference on Autonomic and Autonomous Systems, 2008. ICAS 2008, March 16-21, pp. 88–93 (2008)
Fast artificial neural network library (fann), http://leenissen.dk/fann/
Nissen, S.: Large scale reinforcement learning using q-sarsa and cascading neural networks. M.Sc. Thesis (October 2007)
Fahlman, S.E. et al.: The cascade 2 learning architecture. technical report cmu-cs-tr-96-184, carnegie mellon university
De Winter, D., et al.: A hybrid thin-client protocol for multimedia streaming and interactive gaming applications. In: Proceedings of Network and Operating Systems Support for Digital Audio and Video 2006 (NOSSDAV 2006), pp. 86–92 (2006)
Ns-2, The Network Simulator, http://www.isi.edu/nsnam/ns/
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Latré, S. et al. (2008). Automated Generation of Knowledge Plane Components for Multimedia Access Networks. In: van der Meer, S., Burgess, M., Denazis, S. (eds) Modelling Autonomic Communications Environments. MACE 2008. Lecture Notes in Computer Science, vol 5276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87355-6_5
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DOI: https://doi.org/10.1007/978-3-540-87355-6_5
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