Efficient Embedding VNFs in 5G Network Slicing: A Deep Reinforcement Learning Approach

TL;DR

This paper introduces a deep reinforcement learning approach called Deep Allocation Agent (DAA) for efficient embedding of virtual network functions in 5G network slicing, significantly improving slice accommodation rates under resource constraints.

Contribution

The paper proposes a novel deep reinforcement learning scheme for RAN slicing that optimizes VNF placement to maximize slice accommodation in resource-limited 5G networks.

Findings

DAA achieves over 80% success rate in resource-limited conditions.

DAA maintains about 60% success rate under extreme resource scarcity.

The approach outperforms traditional heuristic methods in slice embedding efficiency.

Abstract

5G radio access network (RAN) slicing aims to logically split an infrastructure into a set of self-contained programmable RAN slices, with each slice built on top of the underlying physical RAN (substrate) is a separate logical mobile network, which delivers a set of services with similar characteristics. Each RAN slice is constituted by various virtual network functions (VNFs) distributed geographically in numerous substrate nodes. A key challenge in building a robust RAN slicing is, therefore, designing a RAN slicing (RS)-configuration scheme that can utilize information such as resource availability in substrate networks as well as the interdependent relationships among slices to map (embed) VNFs onto live substrate nodes. With such motivation, we propose a machine-learning-powered RAN slicing scheme that aims to accommodate maximum numbers of slices (a set of connected Virtual Network Functions - VNFs) within a given request set. More specifically, we present a deep reinforcement scheme that is called Deep Allocation Agent (DAA). In short, DAA utilizes an empirically designed deep neural network that observes the current states of the substrate network and the requested slices to schedule the slices of which VNFs are then mapped to substrate nodes using an optimization algorithm. DAA is trained towards the goal of maximizing the number of accommodated slices in the given set by using an explicitly designed reward function. Our experiment study shows that, on average, DAA is able to maintain a rate of successfully routed slices above 80% in a resource-limited substrate network, and about 60% in extreme conditions, i.e., the available resources are much less than the demands.