Research Article
A Reliable Service Function Chain Orchestration Method Based on Federated Reinforcement Learning
@INPROCEEDINGS{10.1007/978-3-031-24383-7_10, author={Zhiwen Xiao and Tao Tao and Zhuo Chen and Meng Yang and Jing Shang and Zhihui Wu and Zhiwei Guo}, title={A Reliable Service Function Chain Orchestration Method Based on Federated Reinforcement Learning}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 18th EAI International Conference, CollaborateCom 2022, Hangzhou, China, October 15-16, 2022, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2023}, month={1}, keywords={Service function chain Cloud-edge collaborative computing Federated reinforcement learning Reliability}, doi={10.1007/978-3-031-24383-7_10} }- Zhiwen Xiao
Tao Tao
Zhuo Chen
Meng Yang
Jing Shang
Zhihui Wu
Zhiwei Guo
Year: 2023
A Reliable Service Function Chain Orchestration Method Based on Federated Reinforcement Learning
COLLABORATECOM
Springer
DOI: 10.1007/978-3-031-24383-7_10
Abstract
The novel cloud-edge collaborative computing architecture can provide more efficient and intelligent services close to users. Reliable service function chain orchestration among datacenters is critical to ensuring computing efficiency. In this study, a service orchestration model is proposed to improve the reliability while reducing cost. The solution is a federated reinforcement learning framework that shares decision-making experiences to obtain reliable and effective service orchestration results between different datacenter environments. The simulation results demonstrate that the proposed orchestration method reaches convergence faster and has a significant performance in terms of improving service reliability.
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