Enhancing a Fuzzy Logic Inference Engine through Machine Learning for a Self- Managed Network

Authors:
Panagis Magdalinos;Apostolos Kousaridas;Panagiotis Spapis;George Katsikas;Nancy Alonistioti
Affiliations:
Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece 157-84;Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece 157-84;Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece 157-84;Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece 157-84;Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece 157-84
Venue:
Mobile Networks and Applications
Year:
2011

Citing 12
Cited 0

The Vision of Autonomic Computing

Computer
When Is ''Nearest Neighbor'' Meaningful?

ICDT '99 Proceedings of the 7th International Conference on Database Theory
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
A survey of autonomic communications

ACM Transactions on Autonomous and Adaptive Systems (TAAS)
A Framework for In-Network Management in Heterogeneous Future Communication Networks

MACE '08 Proceedings of the 3rd IEEE international workshop on Modelling Autonomic Communications Environments
Multi-agent Reinforcement Learning in Network Management

AIMS '09 Proceedings of the 3rd International Conference on Autonomous Infrastructure, Management and Security: Scalability of Networks and Services
Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison

Performance Evaluation
Transmission power regulation in cooperative cognitive radio systems under uncertainties

ISWPC'10 Proceedings of the 5th IEEE international conference on Wireless pervasive computing
An architecture for autonomic management of ambient networks

AN'06 Proceedings of the First IFIP TC6 international conference on Autonomic Networking
Applications of Machine Learning to Cognitive Radio Networks

IEEE Wireless Communications
Cognitive networks: adaptation and learning to achieve end-to-end performance objectives

IEEE Communications Magazine
Towards autonomic management of communications networks

IEEE Communications Magazine

Quantified Score

Hi-index	0.00

Visualization

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.