Curator: Self-Managing Storage for Enterprise Clusters

Modern cluster storage systems perform a variety of background tasks to improve the performance, availability, durability, and cost-efficiency of stored data. For example, cleaners compact fragmented data to generate long sequential runs, tiering services automatically migrate data between solid-state and hard disk drives based on usage, recovery mechanisms replicate data to improve availability and durability in the face of failures, cost saving techniques perform data transformations to reduce the storage costs, and so on. In this work, we present Curator, a background MapReduce-style execution framework for cluster management tasks, in the context of a distributed storage system used in enterprise clusters. We describe Curator’s design and implementation, and evaluate its performance using a handful of relevant metrics. We further report experiences and lessons learned from its five-year construction period, as well as thousands of customer deployments. Finally, we propose a machine learning-based model to identify an efficient execution policy for Curator’s management tasks that can adapt to varying workload characteristics.