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We consider in this work Edge Computing (EC) in a multi-tenant environment:the resource owner, i.e., the Network Operator (NO), virtualizes the resourcesand lets third party Service Providers (SPs - tenants) run their services,which can be diverse and with heterogeneous requirements. Due toconfidentiality guarantees, the NO cannot observe the nature of the traffic ofSPs, which is encrypted. This makes resource allocation decisions challenging,since they must be taken based solely on observed monitoring information. Wefocus 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 partitioncache among several SPs in order to minimize the upstream traffic. Our goal isto optimize cache allocation using purely data-driven, model-free ReinforcementLearning (RL). Differently from most applications of RL, in which the decisionpolicy is learned offline on a simulator, we assume no previous knowledge isavailable 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 systemand monitoring how its performance changes. Since perturbations generatespurious traffic, we also limit them. We show in simulation that our methodrapidly converges toward the theoretical optimum, we study its fairness, itssensitivity to several scenario characteristics and compare it with a methodfrom the state-of-the-art.

Cache Allocation in Multi-Tenant Edge Computing via online Reinforcement Learning