NDLI: Meta-Learning for Realizing Self-x Management of Future Networks

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Manzoor Ahmed Khan Hamidou Tembine
Abstract	In this paper, we propose an autonomic network management and policy execution framework. The proposed framework refactors the network functionalities by decomposing the network architecture into hierarchical layered architecture. This paper aims at enabling the transition from a rule-based control structure to a more distributed and autonomic network control by implementing the self-x or self-* learning vision on each layer. The problem is modeled using multi-layer dynamic games. At each layer, a self-* learning procedure is proposed to learn and adapt the reverse Stackelberg policies. To validate the proposed framework, we develop a full scale demonstrator comprising of flat IP core and heterogeneous wireless access networks. We have also developed various tools and software agents to implement the self-x management vision. The proposed self-x learning is implemented via mobile intelligent agents in a distributed fashion. Our experimental results show quick re-stabilization of the self-* learning in mobile intelligent agents and the observed performance remain well above the satisfactory values for different key performance indicator with the proposed meta-learning approach.
e-ISSN	21693536
DOI	10.1109/ACCESS.2017.2745999
Journal	IEEE Access
Volume Number	5
Language	English
Publisher	IEEE
Publisher Date	2017-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Autonomous Network Management Game Theory Heterogeneous Wireless Networks Self-organizing Networks Software Agents
Content Type	Text
Resource Type	Article

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