Reinforcement Learning based QoS/QoE-aware Service Function Chaining in Software-Driven 5G Slices

TL;DR

This paper introduces a reinforcement learning framework for service function chaining in 5G networks that optimizes user experience and quality of service using SDN/NFV technologies.

Contribution

It proposes a novel DQN-based agent framework that considers QoE and QoS for dynamic service chaining in 5G slices, with an efficient QoS information collection method.

Findings

Framework achieves high QoE provisioning

Maintains QoS constraints effectively

Performs well in dynamic network environments

Abstract

With the ever growing diversity of devices and applications that will be connected to 5G networks, flexible and agile service orchestration with acknowledged QoE that satisfies end-user's functional and QoS requirements is necessary. SDN (Software-Defined Networking) and NFV (Network Function Virtualization) are considered key enabling technologies for 5G core networks. In this regard, this paper proposes a reinforcement learning based QoS/QoE-aware Service Function Chaining (SFC) in SDN/NFV-enabled 5G slices. First, it implements a lightweight QoS information collector based on LLDP, which works in a piggyback fashion on the southbound interface of the SDN controller, to enable QoS-awareness. Then, a DQN (Deep Q Network) based agent framework is designed to support SFC in the context of NFV. The agent takes into account the QoE and QoS as key aspects to formulate the reward so that it is expected to maximize QoE while respecting QoS constraints. The experiment results show that this framework exhibits good performance in QoE provisioning and QoS requirements maintenance for SFC in dynamic network environments.