ali nouruzi

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 is 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

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 is 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

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 is 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

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 is 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