… In this paper, we address the Virtual Network Function (VNF) placement problem by
developing a machine learning decision tree model that learns from the effective placement of the …

… a neural-network model (ie, a subset of machine learning) that … function of the network traffic
they should process. Based on the traffic traces collected over a commercial mobile network, …

… In this work, we first propose machine learning models, … network function instances based
on the traffic demand, using the traffic traces collected over a real-operator commercial network…

… In this work, we solve the VNF-FGE problem by means of a Reinforcement Learning approach
to model a placement policy in a Network Function Virtualization infrastructure. For that …

… network into a … machine learning-based VNF placement and readjustment algorithms
to efficiently deploy and migrate VNFs across physical servers. Note that many machine-learning-…

… By increasing the capacity, we allow one or more requests to share the same network
function image. Reducing the capacity, we aim at a more concise and efficient use of the server …

… We then use these machine learning models inside existing VNF placement algorithms for
improved dynamic resource allocation. These algorithms – irrespective of which model for …

… hardware-based middleboxes at the suitable location inside the network and controlled
traffic flows to provide the various network functions. Network Function Virtualization (NFV) [1] …

… , a reinforcement learning (RL) approach to efficiently find device placements for distributed
neural network training. Unlike prior approaches that only find a device placement for a …

… network functions allows optimizing their placement in the virtual data center. We will optimize
the placement of the network functions … by using Kokhonen's network by the following …