I-D Action: draft-ihsan-nmrg-rl-vne-ps-02.txt

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        Title           : Reinforcement Learning-Based Virtual Network Embedding: Problem Statement
        Authors         : Ihsan Ullah
                          Youn-Hee Han
                          TaeYeon Kim
	Filename        : draft-ihsan-nmrg-rl-vne-ps-02.txt
	Pages           : 19
	Date            : 2022-04-21

Abstract:
   In Network virtualization (NV) technology, Virtual Network Embedding
   (VNE) is an algorithm used to map a virtual network to the substrate
   network.  VNE is the core orientation of NV which has a great impact
   on the performance of virtual network and resource utilization of the
   substrate network.  An efficient embedding algorithm can maximize the
   acceptance ratio of virtual networks to increase the revenue for
   Internet service provider.  Several works have been appeared on the
   design of VNE solutions, however, it has becomes a challenging issues
   for researchers.  To solved the VNE problem, we believe that
   reinforcement learning (RL) can play a vital role to make the VNE
   algorithm more intelligent and efficient.  Moreover, RL has been
   merged with deep learning techniques to develop adaptive models with
   effective strategies for various complex problems.  In RL, agents can
   learn desired behaviors (e.g, optimal VNE strategies), and after
   learning and completing training, it can embed the virtual network to
   the subtract network very quickly and efficiently.  RL can reduce the
   complexity of the VNE algorithm, however, it is too difficult to
   apply RL techniques directly to VNE problems and need more research
   study.  In this document, we presenting a problem statement to
   motivate the researchers toward the VNE problem using deep
   reinforcement learning.


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https://datatracker.ietf.org/doc/draft-ihsan-nmrg-rl-vne-ps/

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