  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.
