Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning

TL;DR

This paper introduces a Deep Q-Network based framework for online service provisioning in NFV-enabled networks, optimizing resource use and QoS fulfillment amidst stochastic requests and limited resources.

Contribution

It proposes a novel DQN-AR method for adaptive resource allocation, considering service characteristics and network constraints, improving request admission and reducing costs.

Findings

Request admission increases by 7-14%.

Network utilization cost decreases by 5-20%.

Framework effectively manages stochastic service requests.

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-enable 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 %.