  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.
