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R. A. Addad, D. L. C. Dutra, T. Taleb and H. Flinck, "AI-Based Network-Aware Service Function Chain Migration in 5G and Beyond Networks," in IEEE Transactions on Network and Service Management, vol. 19, no. 1, pp. 472-484, March 2022, doi: 10.1109/TNSM.2021.3074618

AI-based network-aware service function chain migration in 5G and beyond networks

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Author: Addad, Rami Akrem1; Dutra, Diego Leonel Cadette2; Taleb, Tarik3,4,5;
Organizations: 1Department of Communications and Networking, Aalto University, 02150 Espoo, Finland
2PESC, Federal University of Rio de Janeiro, Rio de Janeiro 21941-972, Brazil
3COMNET, Aalto University, Espoo 02710, Finland
4Centre for Wireless Communications, University of Oulu, 90570 Oulu, Finland
5Computer and Information Security Department, Sejong University, Seoul 05006, South Korea
6Nokia Bell Labs, 02610 Espoo, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 34.4 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-08-30


While the 5G network technology is maturing and the number of commercial deployments is growing, the focus of the networking community is shifting to services and service delivery. 5G networks are designed to be a common platform for very distinct services with different characteristics. Network Slicing has been developed to offer service isolation between the different network offerings. Cloud-native services that are composed of a set of inter-dependent micro-services are assigned into their respective slices that usually span multiple service areas, network domains, and multiple data centers. Due to mobility events caused by moving end-users, slices with their assigned resources and services need to be re-scoped and re-provisioned. This leads to slice mobility whereby a slice moves between service areas and whereby the inter-dependent service and resources must be migrated to reduce system overhead and to ensure low-communication latency by following end-user mobility patterns. Recent advances in computational hardware, Artificial Intelligence, and Machine Learning have attracted interest within the communication community to study and experiment self-managed network slices. However, migrating a service instance of a slice remains an open and challenging process, given the needed co-ordination between inter-cloud resources, the dynamics, and constraints of inter-data center networks. For this purpose, we introduce a Deep Reinforcement Learning based agent that is using two different algorithms to optimize bandwidth allocations as well as to adjust the network usage to minimize slice migration overhead. We show that this approach results in significantly improved Quality of Experience. To validate our approach, we evaluate the agent under different configurations and in real-world settings and present the results.

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Series: IEEE transactions on network and service management
ISSN: 2373-7379
ISSN-E: 1932-4537
ISSN-L: 2373-7379
Volume: 19
Issue: 1
Pages: 472 - 484
DOI: 10.1109/tnsm.2021.3074618
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research work is partially supported by the European Union’s Horizon 2020 ICT Cloud Computing program under the ACCORDION project with grant agreement No. 871793 and by the European Union’s Horizon 2020 research and innovation program under the CHARITY project with grant agreement No. 101016509. It is also partially funded by the Academy of Finland Project 6Genesis under grant agreements No. 318927.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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