Achieving AI-enabled Robust End-to-End Quality of Experience over Radio Access Networks

TL;DR

This paper introduces a machine learning framework for optimizing resource allocation in radio access networks to enhance end-to-end quality of experience for emerging applications like AR and remote surgery.

Contribution

It presents a novel deep learning-based approach for modeling and robustly optimizing resource allocation, enabling efficient and resilient E2E QoE management in complex networks.

Findings

Deep learning models achieve 99.8% accuracy in predicting E2E QoE.

The proposed robust optimization outperforms existing differential services.

Framework supports graceful degradation when resources are limited.

Abstract

Emerging applications such as Augmented Reality, the Internet of Vehicles and Remote Surgery require both computing and networking functions working in harmony. The End-to-end (E2E) quality of experience (QoE) for these applications depends on the synchronous allocation of networking and computing resources. However, the relationship between the resources and the E2E QoE outcomes is typically stochastic and non-linear. In order to make efficient resource allocation decisions, it is essential to model these relationships. This article presents a novel machine-learning based approach to learn these relationships and concurrently orchestrate both resources for this purpose. The machine learning models further help make robust allocation decisions regarding stochastic variations and simplify robust optimization to a conventional constrained optimization. When resources are insufficient to accommodate all application requirements, our framework supports executing some of the applications with minimal degradation (graceful degradation) of E2E QoE. We also show how we can implement the learning and optimization methods in a distributed fashion by the Software-Defined Network (SDN) and Kubernetes technologies. Our results show that deep learning-based modelling achieves E2E QoE with approximately 99.8\% accuracy, and our robust joint-optimization technique allocates resources efficiently when compared to existing differential services alternatives.