Abstract:
Many services replicate data for fault-tolerant storage of the data and high-availability of the service. When deployed in the cloud, the replication performed by these services provides the desired high-availability but does not provide significant additional fault-tolerance for the data. This is because cloud deployments use fault-tolerant storage services instead of the simple local disks that many replicated data services were designed to use. Because the cloud storage services already provide fault-tolerance for the data, the extra replicas create unnecessary cost in running the service. However, replication is still needed for high-availability of the service itself. In this paper, we explore types of replicated data services and how they can be mapped onto various classes of cloud storage. We then propose a general architectural pattern that can be used to: (1) limit additional storage resulting in monetary cost saving, (2) while keeping the same performance for the service, and (3) maintaining the same high-availability of the services and the durability guarantees for the data. We prototype our approach in two popular open-source replicated data services, Kafka and Cassandra, and show that with relatively little modification these systems can be deployed for a fraction of the storage cost without affecting the availability guarantees, durability guarantees, or performance.
