Research Article
Service-Aware Virtual Network Function Migration Based on Deep Reinforcement Learning
@INPROCEEDINGS{10.1007/978-3-030-99200-2_36, author={Zeming Li and Ziyu Liu and Chengchao Liang and Zhanjun Liu}, title={Service-Aware Virtual Network Function Migration Based on Deep Reinforcement Learning}, proceedings={Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings}, proceedings_a={CHINACOM}, year={2022}, month={4}, keywords={Network Function Virtualization Virtual Network Functions Markov decision process Migration Deep reinforcement learning}, doi={10.1007/978-3-030-99200-2_36} }- Zeming Li
Ziyu Liu
Chengchao Liang
Zhanjun Liu
Year: 2022
Service-Aware Virtual Network Function Migration Based on Deep Reinforcement Learning
CHINACOM
Springer
DOI: 10.1007/978-3-030-99200-2_36
Abstract
Network Function Virtualization (NFV) aims to provide a way to build agile and flexible networks by building a new paradigm of provisioning network services where network functions are virtualized as Virtual Network Functions (VNFs). Network services are implemented by service function chains, which are formed by a series of VNFs with a specific traversal order. VNF migration is a critical procedure to reconfigure VNFs for providing better network services. However, the migration of VNFs for dynamic service requests is a key challenge. Most VNF migration works mainly focused on static threshold trigger mechanism which will cause frequent migration. Therefore, we propose a novel mechanism to solve the issue in this paper. With the objective of minimizing migration overhead, a stochastic optimization problem based on Markov decision process is formulated. Moreover, we prove the NP-hardness of the problem and propose a service-aware VNF migration scheme based on deep reinforcement learning. Extensive simulations are conducted that the proposed scheme can effectively avoid frequent migration and reduce the migration overhead.
