University of Oulu

J. Prados-Garzon, T. Taleb and M. Bagaa, "LEARNET: Reinforcement Learning Based Flow Scheduling for Asynchronous Deterministic Networks," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9149092

LEARNET : reinforcement learning based flow scheduling for asynchronous deterministic networks

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Author: Prados-Garzon, Jonathan1; Taleb, Tarik1,2; Bagaa, Miloud1
Organizations: 1Aalto University, Espoo, Finland
2University of Oulu, 90570 Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 10 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022032124194
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2022-03-21
Description:

Abstract

Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet) standards come to satisfy the needs of many industries for deterministic network services. That is the ability to establish a multi-hop path over an IP network for a given flow with deterministic Quality of Service (QoS) guarantees in terms of latency, jitter, packet loss, and reliability. In this work, we propose a reinforcement learning-based solution, which is dubbed LEARNET, for the flow scheduling in deterministic asynchronous networks. The solution leverages predictive data analytics and reinforcement learning to maximize the network operator’s revenue. We evaluate the performance of LEARNET through simulation in a fifth-generation (5G) asynchronous deterministic backhaul network where incoming flows have characteristics similar to the four critical 5GQoS Identifiers (5QIs) defined in Third Generation Partnership Project (3GPP) TS 23.501 V16.1.0. Also, we compared the performance of LEARNET with a baseline solution that respects the 5QIs priorities for allocating the incoming flows. The obtained results show that, for the scenario considered, LEARNET achieves a gain in the revenue of up to 45% compared to the baseline solution.

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Series: IEEE International Conference on Communications
ISSN: 1550-3607
ISSN-E: 1938-1883
ISSN-L: 1550-3607
ISBN: 978-1-7281-5089-5
ISBN Print: 978-1-7281-5090-1
Pages: 1 - 6
Article number: 9149092
DOI: 10.1109/ICC40277.2020.9149092
OADOI: https://oadoi.org/10.1109/ICC40277.2020.9149092
Host publication: ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
Conference: IEEE International Conference on Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work is partially supported by Business Finland 5G-FORCE project and the Academy of Finland 6Genesis and CSN projects with grant agreement No. 318927 and No. 311654, respectively.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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