  We consider in this work Edge Computing (EC) in a multi-tenant environment:
the resource owner, i.e., the Network Operator (NO), virtualizes the resources
and lets third party Service Providers (SPs - tenants) run their services,
which can be diverse and with heterogeneous requirements. Due to
confidentiality guarantees, the NO cannot observe the nature of the traffic of
SPs, which is encrypted. This makes resource allocation decisions challenging,
since they must be taken based solely on observed monitoring information. We
focus on one specific resource, i.e., cache space, deployed in some edge node,
e.g., a base station. We study the decision of the NO about how to partition
cache among several SPs in order to minimize the upstream traffic. Our goal is
to optimize cache allocation using purely data-driven, model-free Reinforcement
Learning (RL). Differently from most applications of RL, in which the decision
policy is learned offline on a simulator, we assume no previous knowledge is
available to build such a simulator. We thus apply RL in an \emph{online}
fashion, i.e., the policy is learned by directly perturbing the actual system
and monitoring how its performance changes. Since perturbations generate
spurious traffic, we also limit them. We show in simulation that our method
rapidly converges toward the theoretical optimum, we study its fairness, its
sensitivity to several scenario characteristics and compare it with a method
from the state-of-the-art.
