A neuro-fuzzy approach to self-management of virtual network resources

Network virtualisation promises to lead to better manageability of the future Internet by allowing for adaptable sharing of physical network resources among different virtual networks. However, the sharing of resources is not trivial as virtual nodes and links should first be mapped onto substrate nodes and links, and thereafter the allocated resources managed throughout the lifetime of the virtual network. In this paper, we design and evaluate reinforcement learning-based neuro-fuzzy algorithms that perform dynamic, decentralised and coordinated self-management of substrate network resources. The objective is to achieve better efficiency in the utilisation of substrate network resources while ensuring that the quality of service requirements of the virtual networks are not violated. The proposed algorithms are evaluated through comparisons with a Q-learning-based approach as well as two static resource allocation schemes.