A Dynamic Shortest Path Deployment of Virtual Network Function

DOI

10.2991/mecae-18.2018.130How to use a DOI?

Keywords

5G, network function virtualization, vEPC, service function chaining, Q-learning.

Abstract

In the 5G mobile communication network virtualization scenario, how to deploy service function chaining of the core network efficiently is the key problem to realize the efficient deployment of virtual Evolved Packet Core network services. In order to solve the problem that the existing deployment methods are difficult to meet the requirement of the mobile communication with low latency, this paper proposed a method for service function chaining deployment based on Q-learning. This method solved the problem by applying establish a Markov decision process model to the latency optimization in the context of VNF deployment, and then design a Q-learning algorithm to found the deployment solutions with minimum delay cost of network services. Simulation results show that the proposed method achieves better performances in terms of average processing time, request acceptance rate, gain and execution time.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaolei Wang
AU  - Shiqing Sun
AU  - Hongbo Tang
PY  - 2018/03
DA  - 2018/03
TI  - A Dynamic Shortest Path Deployment of Virtual Network Function
BT  - Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
SP  - 280
EP  - 290
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-18.2018.130
DO  - 10.2991/mecae-18.2018.130
ID  - Wang2018/03
ER  -