Data centers are nowadays ubiquitous, in a worldwide scale, and often geographically dispersed. In such environments, data reliability and availability are enhanced via data redundancy throughout the distributed storage. Because user performance is important in data centers, data updates in such distributed environments are done such that eventual consistency is achieved. In this paper we utilize a learning-based framework that aims at scheduling data writes during user idle times such that the impact on user performance is limited within strict predefined targets while the updates are completed as fast as possible. The effectiveness and robustness of the proposed framework are illustrated via extensive trace-driven simulations.
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