Dynamic Topology Design of NFV-Enabled Services Using Deep Reinforcement Learning

Abstract

Next-generation networks are endowed with enhanced capabilities thanks to software-defined networking and network function virtualization (NFV). There is a radical shift from device-centric to experience-driven environments of which data is the primary driver behind its running engines. In this paper, we consider joint topology design, traffic routing and NF placement for unicast NFV-enabled services. We develop an end-to-end model-free deep reinforcement learning (RL) framework to dynamically allocate processing and transmission resources, while considering time-varying network traffic patterns. First, we provide a flexible pre-processing technique that represents and reduces the state space and action space of the considered joint problem for the deep RL algorithm. Second, we present a deep deterministic policy gradient (DDPG) algorithm that is enhanced with a model-assisted exploration procedure. Due to the multiple resource types with strongly adverse effects, the existing vanilla DDPG algorithm cannot achieve consistent performance. The model-assisted exploration procedure, which utilizes a perturbed step-wise sub-optimal integer linear program, bootstraps and stabilizes the vanilla DDPG algorithm and finds optimal solutions efficiently.