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Rethinking reinforcement learning for cloud elasticity

Cloud elasticity, i.e., the dynamic allocation of resources to applications to meet fluctuating workload demands, has been one of the greatest challenges in cloud computing. Approaches based on reinforcement learning have been proposed but they require a large number of states in order to model complex application behavior. In this work we propose a novel reinforcement learning approach that employs adaptive state space partitioning. The idea is to start from one state that represents the entire environment and partition this into finer-grained states adaptively to the observed workload and system behavior following a decision-tree approach. We explore novel statistical criteria and strategies that decide both the correct parameters and the appropriate time to perform the partitioning.