  As the cloud is pushed to the edge of the network, resource allocation for
user experience improvement in mobile edge clouds (MEC) is increasingly
important and faces multiple challenges. This paper studies quality of
experience (QoE)-oriented resource allocation in MEC while considering user
diversity, limited resources, and the complex relationship between allocated
resources and user experience. We introduce a closed-loop online resource
allocation (CORA) framework to tackle this problem. It learns the objective
function of resource allocation from the historical dataset and updates the
learned model using the online testing results. Due to the learned objective
model is typically non-convex and challenging to solve in real-time, we
leverage the Lyapunov optimization to decouple the long-term average constraint
and apply the prime-dual method to solve this decoupled resource allocation
problem. Thereafter, we put forth a data-driven optimal online queue resource
allocation (OOQRA) algorithm and a data-driven robust OQRA (ROQRA) algorithm
for homogenous and heterogeneous user cases, respectively. Moreover, we provide
a rigorous convergence analysis for the OOQRA algorithm. We conduct extensive
experiments to evaluate the proposed algorithms using the synthesis and YouTube
datasets. Numerical results validate the theoretical analysis and demonstrate
that the user complaint rate is reduced by up to 100% and 18% in the synthesis
and YouTube datasets, respectively.
