Proposal and investigation of an ETSI NFV architecture supporting aI-based resource prediction

The high reconfiguration time of cloud resources in Network Function Virtualization architecture has led to make ineffective the reactive cloud resource allocation procedures whose application lead to over-allocate resources or to degrade Quality of Service in decreasing/increasing traffic scenario. Recently many Artificial Intelligence (AI)-based allocation procedures have been proposed to pre-allocate cloud resource according to required processing capacity predictions. In this paper we illustrate how an ETSI NFV architecture can support these predictions procedures. Furthermore they aim to exactly predict the processing capacity to be allocated and they are all based on the minimization of a symmetric loss function of the neural network. For this reason we propose a resource allocation procedure with a asymmetric loss function whose parameters are dependent on an overall cost expressed in terms of allocation and QoS degradation costs. We prove that the proposed solution allows for a cost reduction in the order of 30% in a typical NFV traffic and network scenario.