The Optimized Deployment of Service Function Chain Based on Reinforcement Learning

Authors

  • Yibo Zhang

DOI:

https://doi.org/10.54097/ivvdqt8l76

Keywords:

Service Function Chain, Reinforcement learning, Network Function Virtualization

Abstract

With the rapid development and application of technologies such as artificial intelligence, the Internet of Things, and cloud computing, data is showing explosive growth. In order to address the rising energy consumption due to the increasing number of devices in the traditional network architecture, software-defined networking and network function virtualization have been proposed. In this paper, we propose a reinforcement learning model based on actor-critic architecture. The service function chain deployment problem is mathematically modeled, and minimizing the total service function chain delay is taken as the optimization objective. The experimental results demonstrate that the service function chain deployment algorithm proposed in this paper is improved in terms of total system latency.

References

Sherry J, Hasan S, Scott C, et al. Making middleboxes someone else's problem: Network processing as a cloud service[J]. ACM SIGCOMM Computer Communication Review, 2012, 42(4): 13-24.

Mijumbi R, Serrat J, Gorricho J L, et al. Network function virtualization: State-of-the-art and research challenges[J]. IEEE Communications surveys & tutorials, 2015, 18(1): 236-262.

Németh B, Molner N, Martín-Pérez J, et al. Delay and reliability-constrained VNF placement on mobile and volatile 5G infrastructure[J]. IEEE Transactions on Mobile Computing, 2021, 21(9): 3150-3162.

Hejja K, Hesselbach X. Offline and online power aware resource allocation algorithms with migration and delay constraints[J]. Computer networks, 2019, 158: 17-34.

Addis B, Belabed D, Bouet M, et al. Virtual network functions placement and routing optimization[C]//2015 IEEE 4th International Conference on Cloud Networking (CloudNet). IEEE, 2015: 171-177.

Ghribi C, Mechtri M, Zeghlache D. A dynamic programming algorithm for joint VNF placement and chaining[C]//Proceedings of the 2016 ACM Workshop on Cloud-Assisted Networking. 2016: 19-24.

Soualah O, Mechtri M, Ghribi C, et al. Online and batch algorithms for VNFs placement and chaining[J]. Computer Networks, 2019, 158: 98-113.

Shen G, Li Q, Jiang Y, et al. A four-stage adaptive scheduling scheme for service function chain in NFV[J]. Computer Networks, 2020, 175: 107259.

Kumaraswamy S, Nair M K. Bin packing algorithms for virtual machine placement in cloud computing: a review[J]. International Journal of Electrical and Computer Engineering, 2019, 9(1): 512.

Addis B, Belabed D, Bouet M, et al. Virtual network functions placement and routing optimization[C]//2015 IEEE 4th International Conference on Cloud Networking (CloudNet). IEEE, 2015: 171-177.

Downloads

Published

28-02-2024

Issue

Section

Articles

How to Cite

Zhang, Y. (2024). The Optimized Deployment of Service Function Chain Based on Reinforcement Learning. Journal of Computing and Electronic Information Management, 12(1), 37-41. https://doi.org/10.54097/ivvdqt8l76

Similar Articles

1-10 of 96

You may also start an advanced similarity search for this article.