Ali Nouruzi
Online service provisioning in NFV-enabled networks using deep reinforcement learning
Nouruzi, Ali; Zakeri, Abolfazl; Javan, Mohammad Reza; Mokari, Nader; Hussain, Rasheed; Kazmi, S. M. Ahsan
Authors
Abolfazl Zakeri
Mohammad Reza Javan
Nader Mokari
Rasheed Hussain
Ahsan Kazmi Ahsan.Kazmi@uwe.ac.uk
Senior Lecturer in Computer Science
Abstract
In this paper, we study a Deep Reinforcement Learning (DRL) based framework for an online end-user service provisioning in a Network Function Virtualization (NFV)-enabled network. We formulate an optimization problem aiming to minimize the cost of network resource utilization. The main challenge is provisioning the online service requests by fulfilling their Quality of Service (QoS) under limited resource availability. Moreover, fulfilling the stochastic service requests in a large network is another challenge that is evaluated in this paper. To solve the formulated optimization problem in an efficient and intelligent manner, we propose a Deep Q-Network for Adaptive Resource allocation (DQN-AR) in NFV-enabled network for function placement and dynamic routing which considers the available network resources as DQN states. Moreover, the service's characteristics, including the service life time and number of the arrival requests, are modeled by the Uniform and Exponential distribution, respectively. In addition, we evaluate the computational complexity of the proposed method. Numerical results carried out for different ranges of parameters reveal the effectiveness of our framework. In specific, the obtained results show that the average number of admitted requests of the network increases by 7 up to 14% and the network utilization cost decreases by 5 and 20%.
Journal Article Type | Article |
---|---|
Acceptance Date | Feb 15, 2022 |
Online Publication Date | Mar 15, 2022 |
Publication Date | Sep 1, 2022 |
Deposit Date | Nov 9, 2022 |
Publicly Available Date | Jan 31, 2023 |
Journal | IEEE Transactions on Network and Service Management |
Print ISSN | 1932-4537 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 19 |
Issue | 3 |
Pages | 3276-3289 |
DOI | https://doi.org/10.1109/TNSM.2022.3159670 |
Keywords | Electrical and Electronic Engineering, Computer Networks and Communications, Deep reinforcement learning, Service lifetime, Resource allocation, NFV |
Public URL | https://uwe-repository.worktribe.com/output/10109168 |
Publisher URL | https://ieeexplore.ieee.org/document/9734748 |
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Online service provisioning in NFV-enabled networks using deep reinforcement learning
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Copyright Statement
This is the author’s accepted manuscript of the article 'Nouruzi, A., Zakeri, A., Javan, M. R., Mokari, N., Hussain, R., & Kazmi, S. M. A. (2022). Online Service Provisioning in NFV-Enabled Networks Using Deep Reinforcement Learning. IEEE Transactions on Network and Service Management, 19(3), 3276-3289’.
DOI: https://doi.org/10.1109/tnsm.2022.3159670
The final published version is available here: https://ieeexplore.ieee.org/document/9734748
© 2022 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.
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