Deep Q-Networks based Auto-scaling for Service Function Chaining

Abstract

Network function virtualization (NFV) is a key technology of the 5G network era. NFV decouples a network function from proprietary hardware so that the network function can operate on commercial off-the-shelf (COTS) servers as a form of virtual network functions (VNFs). Owing to the advantage of NFV, network functions can be applied dynamically to the networks. However, NFV complicates network management because this technology creates numerous virtual resources that should be managed. To solve the problem of complicated network management, studies on applying artificial intelligence (AI) to the NFV-enabled networks, i.e., VNF life cycle management, have attracted attention. In particular, autoscaling, which is one of the essential functions of VNF life cycle management, adds or removes VNF instances to meet service requirements. It is a challenging task to determine the optimal number of VNF instances in dynamic networks, satisfying service requirements. In this paper, we propose a novel auto-scaling method using reinforcement learning (RL) for scale-in/out of multi-tier VNF instances, i.e., service function chaining (SFC) in NFV environments. The proposed approach defines RL’s states using a status of SFC composed of multi-tier VNF instances and uses service level objectives (SLO) to make a reward model. We validate the proposed approach in an OpenStack environment, and it shows that our proposed auto-scaling method provides the optimal number of VNF instances in each tier while minimizing SLO violation.