University of Oulu

R. A. Addad, D. L. C. Dutra, T. Taleb and H. Flinck, "Toward Using Reinforcement Learning for Trigger Selection in Network Slice Mobility," in IEEE Journal on Selected Areas in Communications, vol. 39, no. 7, pp. 2241-2253, July 2021, doi: 10.1109/JSAC.2021.3078501

Toward using reinforcement learning for trigger selection in network slice mobility

Saved in:
Author: Addad, Rami Akrem1; Dutra, Diego Leonel Cadette2; Taleb, Tarik1,3,4;
Organizations: 1Aalto University, Espoo, Finland
2Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
3Centre for Wireless Communications (CWC), University of Oulu, Oulu, Finland
4Computer and Information Security Department, Sejong University, Seoul, South Korea
5Nokia Bell Labs, Espoo, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021101250684
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-10-12
Description:

Abstract

Recent 5G trials have demonstrated the usefulness of the Network Slicing concept that delivers customizable services to new and under-serviced industry sectors. However, user mobility’s impact on the optimal resource allocation within and between slices deserves more attention. Slices and their dedicated resources should be offered where the services are to be consumed to minimize network latency and associated overheads and costs. Different mobility patterns lead to different resource re-allocation triggers, leading eventually to slice mobility when enough resources are to be migrated. The selection of the proper triggers for resource re-allocation and related slice mobility patterns is challenging due to triggers’ multiplicity and overlapping nature. In this paper, we investigate the applicability of two Deep Reinforcement Learning based algorithms for allowing a fine-grained selection of mobility triggers that may instantiate slice and resource mobility actions. While the first proposed algorithm relies on a value-based learning method, the second one exploits a hybrid approach to optimize the action selection process. We present an enhanced ETSI Network Function Virtualization edge computing architecture that incorporates the studied mechanisms to implement service and slice migration. We evaluate the proposed methods’ efficiency in a simulated environment and compare their performance in terms of training stability, learning time, and scalability. Finally, we identify and quantify the applicability aspects of the respective approaches.

see all

Series: IEEE journal on selected areas in communications
ISSN: 0733-8716
ISSN-E: 1558-0008
ISSN-L: 0733-8716
Volume: 39
Issue: 7
Pages: 2241 - 2253
DOI: 10.1109/JSAC.2021.3078501
OADOI: https://oadoi.org/10.1109/JSAC.2021.3078501
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
5G
Funding: This work is partially supported by the European Union’s Horizon 2020 ICT Cloud Computing program under the ACCORDION project with grant agreement No. 871793. The work is also funded by the Academy of Finland Project CSN under Grant Agreement 311654 and the 6Genesis project under grant No. 318927, respectively.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
Copyright information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.