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%.
Highlights
State of The Art and MotivationNew applications have emerged rapidly with diverse Quality of Service (QoS) requirements [1]
New applications have emerged rapidly with diverse Quality of Service (QoS) requirements [1]. To meet their requirements in an efficient manner with a common physical infrastructure, exploiting advanced technologies is indispensable where these technologies are expected to have pivotal impacts on network performance in terms of enhancing QoS and resource efficiency which result in cost reduction
Motivated by significant effectiveness of Deep Reinforcement Learning (DRL)-based algorithm for Resource Allocation (RA) in Network Function Virtualization (NFV)-enabled networks, we propose a DRL-based algorithm for service provision in an NFV-enabled network
Summary
New applications have emerged rapidly with diverse Quality of Service (QoS) requirements [1] To meet their requirements in an efficient manner with a common physical infrastructure, exploiting advanced technologies is indispensable where these technologies are expected to have pivotal impacts on network performance in terms of enhancing QoS and resource efficiency which result in cost reduction. One such technology is Network Function Virtualization (NFV) providing an array of benefits such as great flexibility, resource efficiency, and cost reduction [2]. This work focuses on a main question which is: how a service provider offers heterogeneous services with a probabilistic lifetime on the common physical resources in a smart and efficient manner?
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