Content area

Abstract

Network function virtualization (NFV) technology is an efficient way to address the increasing difficulty of provisioning and managing network services. However, NFV-related service function chaining (SFC) deployment in multi-domain networks remains challenging, and there is still room for performance improvement. This paper investigates many heuristic algorithms in the same field and proposes a new method for dynamic SFC deployment in a multi-domain network. In our study, we combine a heuristic algorithm with reinforcement learning and divide the complex problem into several parts. This algorithm efficiently gives the SFC deployment scheme in the multi-domain network with subdomain privacy protection requirements and considers the energy savings of the multi-domain networks. Compared with the existing approach, the proposed algorithm has superiorities in terms of deployment success ratio, deployment profit, time efficiency, and energy consumption.

Details

Title
Reinforcement Q-learning enabled energy-efficient service function chain provisioning in multi-domain networks
Pages
58
Publication year
2025
Publication date
Jan 2025
Publisher
Springer Nature B.V.
ISSN
19366442
e-ISSN
19366450
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3203308302
Copyright
Copyright Springer Nature B.V. Jan 2025