Abstract
Existing network management systems have static and predefined rules or parameters, while human intervention is usually required for their update. However, an autonomic network management system that operates in a volatile network environment should be able to adapt continuously its decision making mechanism through learning from the system’s behavior. In this paper, a novel learning scheme based on the network wide collected experience is proposed targeting the enhancement of network elements’ decision making engine. The algorithm employs a fuzzy logic inference engine in order to enable self-managed network elements to identify faults or optimization opportunities. The fuzzy logic engine is periodically updated through the use of two well known data mining techniques, namely k-Means and k-Nearest Neighbor. The proposed algorithm is evaluated in the context of a load identification problem. The acquired results prove that the proposed learning mechanism improves the deduction capability, thus promoting our algorithm as an attractive approach for enhancing the autonomic capabilities of network elements.
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Notes
The full dataset, accompanied by the source code used in the experiments and a detailed description of the testbed is available at http://kandalf.di.uoa.gr/MONAMI/
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Acknowledgment
This work is supported by the European Commission Seventh Framework Programme ICT-2008-224344 through the Self-NET Project (https://www.ict-selfnet.eu). We also wish to thank the special issue editors, as well as the anonymous reviewers for their constructive suggestions and comments.
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Magdalinos, P., Kousaridas, A., Spapis, P. et al. Enhancing a Fuzzy Logic Inference Engine through Machine Learning for a Self- Managed Network. Mobile Netw Appl 16, 475–489 (2011). https://doi.org/10.1007/s11036-011-0327-1
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DOI: https://doi.org/10.1007/s11036-011-0327-1