… learning technology Deep Reinforcement Learning (DRL), the VNF placement problem …
VNF placement policies can be denoted as Ψ = {ψ1,ψ2,..., }. Our goal is to find a VNF placement …

… In this paper, we study the VNF placement problem in SDN/NFV-enabled networks, which is
… deep reinforcement learning, we propose a Double Deep Q Networkbased VNF Placement …

… technique for VNF placement and name it DDAP (Deep Deterministic Automatic Placement),
… In Section II, we review some of the related work on VNF placement DRL in NFV. Then we …

… As a Baseline, we consider an algorithm that rejects any VNF placement request to a MEC,
if the given node has reached 90% of its total utilization capacity, at any given metric. The …

… In this paper, we explore the potential of reinforcement learning techniques for the placement
of VNF-FGs. However, it turns out that even the most well-known learning technique is …

… of the network state, we propose a deep reinforcement learning (DRL) based online SFC
placement method named DDQP (Double Deep Q-networks Placement). Meanwhile, VNFs are …

… 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 …

… This work applies an adaptive, online, deep reinforcement learning approach along with a
serialization-and-backtracking method and a policy gradient based method to handle SFCs …

… by deciding the resource allocation and placement of each VNF in the slice. The scaling …
VNF to create and how much resources to allocate to each instance, while the placement step …

… function (VNF) placement-and-routing (P&R) is particularly difficult. To this end, we propose
a deep reinforcement learning (DRL)-based approach for solving the joint VNF P&R problem …