• LOGIN
Repository logo

BORIS Portal

Bern Open Repository and Information System

  • Publication
  • Projects
  • Funding
  • Research Data
  • Organizations
  • Researchers
  • LOGIN
Repository logo
Unibern.ch
  1. Home
  2. Publications
  3. Deep Reinforcement Learning for Context-Aware Online Service Function Chain Deployment and Migration over 6G Networks
 

Deep Reinforcement Learning for Context-Aware Online Service Function Chain Deployment and Migration over 6G Networks

Options
  • Details
BORIS DOI
10.48620/78497
Date of Publication
2024
Publication Type
Conference Paper
Division/Institute

Institute of Computer...

Author
Wassie, Solomon Fikadie
Institute of Computer Science
Di Maio, Antonioorcid-logo
Institute of Computer Science
Braun, Torstenorcid-logo
Institute of Computer Science
Institute of Computer Science, Communication and Distributed Systems (CDS)
Publisher
Association for Computing Machinery
Language
English
Publisher DOI
10.1145/3672608.3707975
Uncontrolled Keywords

6G Network Architectu...

Cloud Continuum Frame...

Service Orchestrator

Deep reinforcement le...

Description
The Cloud Continuum Framework (CCF) logically integrates distributed
extreme edge, far edge, near edge, and cloud data centers
in 6G networks. Deploying VNFs over the CCF can enhance
network performance and Quality of Service (QoS) for modern
delay-sensitive applications and use cases in 6G networks. Deep
Reinforcement Learning (DRL) has shown potential to automate
Virtual Network Function (VNF) migrations by learning optimal
policies through continuous monitoring of the network environment.
In this work, we leverage Deep Reinforcement Learning to
optimize network control policies that continuously update VNF
placement for optimal Service Function Chain (SFC) deployment
in time-varying user traffic scenarios. By leveraging dynamic VNF
relocation, this approach seeks to improve network performance
in terms of latency, operational costs, scalability, and flexibility.
This study addresses the gap in existing solutions by jointly considering
network performance requirements and migration costs,
providing a more comprehensive strategy for efficient VNF deployment
and management. We show that our proposed DRL-based
VNF deployment method achieves a 28.8% lower delay and a 34%
lower migration overhead compared to state-of-the-art baselines
in a broad range of large-scale simulated scenarios, showing the
proposed method’s scalability features.
Handle
https://boris-portal.unibe.ch/handle/20.500.12422/194098
Project(s)
Service-oriented 6G network architecture for distributed, intelligent, and sustainable cloud-native communication systems (6G-CLOUD)
Funding(s)
SNS-JU 6G Cloud project
Show full item
File(s)
FileFile TypeFormatSizeLicensePublisher/Copright statementContent
Final_Cameraready_version.pdftextAdobe PDF1.49 MBsubmitted
BORIS Portal
Bern Open Repository and Information System
Build: b407eb [23.05. 15:47]
Explore
  • Projects
  • Funding
  • Publications
  • Research Data
  • Organizations
  • Researchers
More
  • About BORIS Portal
  • Send Feedback
  • Cookie settings
  • Service Policy
Follow us on
  • Mastodon
  • YouTube
  • LinkedIn
UniBe logo