I-D Action: draft-ihsan-nmrg-rl-vne-ps-00.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-00.txt
	Pages           : 17
	Date            : 2021-06-14

Abstract:
   In Network virtualization (NV) technology, Virtual Network Embedding
   (VNE) is a problem to map a virtual network to the substrate network.
   It has a great impact on the performance of virtual network and
   resource utilization of the substrate network.  An efficient
   embedding strategy 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, reinforcement learning (RL) can play a vital
   role to make the VNE problem 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 method,
   however, it is too difficult to apply RL techniques directly to VNE
   problems and need more research study.  In this document, we are
   presenting a problem statement to motivate the research community to
   solve the VNE problem using reinforcement learning.


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