A Hard and Soft Hybrid Slicing Framework for Service Level Agreement Guarantee via Deep Reinforcement Learning

TL;DR

This paper introduces a hybrid slicing framework using deep reinforcement learning to ensure SLA guarantees in network slicing, addressing performance loss during training and achieving near-optimal resource allocation.

Contribution

It proposes a novel hybrid slicing strategy with a common slice setting and resource redistribution, ensuring SLA guarantees during DRL training and improving post-convergence performance.

Findings

Guarantees SLA during DRL training phase.

Achieves near-optimal SLA satisfaction ratio.

Improves spectrum utilization and slice isolation.

Abstract

Network slicing is a critical driver for guaranteeing the diverse service level agreements (SLA) in 5G and future networks. Recently, deep reinforcement learning (DRL) has been widely utilized for resource allocation in network slicing. However, existing related works do not consider the performance loss associated with the initial exploration phase of DRL. This paper proposes a new performance-guaranteed slicing strategy with a soft and hard hybrid slicing setting. Mainly, a common slice setting is applied to guarantee slices' SLA when training the neural network. Moreover, the resource of the common slice tends to precisely redistribute to slices with the training of DRL until it converges. Furthermore, experiment results confirm the effectiveness of our proposed slicing framework: the slices' SLA of the training phase can be guaranteed, and the proposed algorithm can achieve the near-optimal performance in terms of the SLA satisfaction ratio, isolation degree and spectrum maximization after convergence.