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

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%.

Citation

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. https://doi.org/10.1109/TNSM.2022.3159670

• 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
• Electronic 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