Enhanced LSTM-based Service Decomposition for Mobile Augmented Reality

TL;DR

This paper introduces a mobility-aware LSTM-based approach for decomposing mobile augmented reality services across devices and edge clouds, optimizing performance and efficiency in 5G networks.

Contribution

It presents a novel LSTM neural network model trained on optimal solutions to enable real-time, proactive service decomposition in mobile augmented reality applications.

Findings

Outperforms existing schemes in decision quality

Reduces computational time significantly

Enhances service and network efficiency

Abstract

Undoubtedly, Mobile Augmented Reality (MAR) applications for 5G and Beyond wireless networks are witnessing a notable attention recently. However, they require significant computational and storage resources at the end device and/or the network via Edge Cloud (EC) support. In this work, a MAR service is considered under the lenses of microservices where MAR service components can be decomposed and anchored at different locations ranging from the end device to different ECs in order to optimize the overall service and network efficiency. To this end, we propose a mobility aware MAR service decomposition using a Long Short Term Memory (LSTM) deep neural network to provide efficient pro-active decision making in real-time. More specifically, the LSTM deep neural network is trained with optimal solutions derived from a mathematical programming formulation in an offline manner. Then, decision making at the inference stage is used to optimize service decomposition of MAR services. A wide set of numerical investigations reveal that the mobility aware LSTM deep neural network manage to outperform recently proposed schemes in terms of both decision making quality as well as computational time.