Replication Degree Customization for High Availability Ming Zhong, Kai Shen, and Joel Seiferas Department of Computer Science, University of Rochester Object replication is a common approach to enhance the availability of distributed data-intensive services and storage systems. Many such systems are known to have highly skewed object request probability distributions. In this paper, we propose an object replication degree customization scheme that maximizes the expected service availability under given object request probabilities, object sizes, and space constraints (e.g., memory/storage capacities). In particular, we discover that the optimal replication degree of an object should be linear in the logarithm of its popularity-to-size ratio. We also study the feasibility and effectiveness of our proposed scheme using applications driven by real-life system object request traces and machine failure traces. When the data object popularity distribution is known a priori, our proposed customization can achieve 1.32-2.92 ``nines'' increase in system availability (or 21-74% space savings at the same availability level) compared to uniform replication. Results also suggest that our scheme requires a moderate amount of replica creation/removal overhead (weekly changes involve no more than 0.24% objects and no more than 0.11% of total data size) under realistic object request popularity changes.