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

Business indexing term
Title
Reinforcement Q-learning enabled energy-efficient service function chain provisioning in multi-domain networks
Publication title
Volume
18
Issue
1
Pages
58
Publication year
2025
Publication date
Jan 2025
Publisher
Springer Nature B.V.
Place of publication
Norwell
Country of publication
Netherlands
Publication subject
ISSN
19366442
e-ISSN
19366450
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2024-12-23
Milestone dates
2024-11-12 (Registration); 2023-12-21 (Received); 2024-09-21 (Accepted)
Publication history
 
 
   First posting date
23 Dec 2024
ProQuest document ID
3203308302
Document URL
https://www.proquest.com/scholarly-journals/reinforcement-q-learning-enabled-energy-efficient/docview/3203308302/se-2?accountid=208611
Copyright
Copyright Springer Nature B.V. Jan 2025
Last updated
2025-05-23
Database
ProQuest One Academic