Oversubscription is an essential cost management strategy for cloud database providers, and its importance is magnified by the emerging paradigm of serverless databases. In contrast to general purpose techniques used for oversubscription in hypervisors, operating systems and cluster managers, we develop techniques that leverage our understanding of how DBMSs use resources and how resource allocations impact database performance. Our techniques are designed to flexibly redistribute resources across database tenants at the node and cluster levels with low overhead. We have implemented our techniques in a commercial cloud database service: Azure SQL Database. Experiments using microbenchmarks, industry-standard benchmarks and real-world resource usage traces show that using our approach, it is possible to tightly control the impact on database performance even with a relatively high degree of oversubscription.

Relational cloud Database-as-a-Service offerings run on multi-tenant infrastructure consisting of clusters of nodes, with each node hosting multiple tenant databases. Such clusters may be over-subscribed to increase resource utilization and improve operational efficiency. When resources are over-subscribed, it is possible that anode has insufficient resources to satisfy the resource demands of all databases on it, making it necessary to move databases to other nodes. Such moves can significantly impact database performance and availability. Therefore, it is important to reduce the likelihood of such resource shortages through judicious placement of databases in the cluster. We propose a novel tenant placement approach that leverages historical traces of tenant resource demands to estimate the probability of resource shortages and leverages these estimates in placement. We have prototyped our techniques in the Service Fabric cluster manager. Experiments using production resource traces from Azure SQL DB and an evaluation on a real cluster deployment show significant improvements over the state-of-the-art.

Microsoft aims to increase the efficiency of Azure SQL DB by maximizing the number of databases that can be hosted in a cluster. However, resource contention among customers increases when changing the configurations, policies, and features that control database co-location on cluster nodes. Tuning and evaluating the efficiency and customer impact of these variables in a scientific manner in production, with a dynamic system and customer workloads, is difficult or infeasible. Here, we present Toto, a benchmark framework for evaluating the efficiency of any cloud service that leverages orchestrators like Service Fabric or Kubernetes. Toto allows for reliable and repeatable specification of a benchmarking scenario of arbitrary scale, complexity, and time-length. An implementation of Toto is deployed in all SQL DB staging clusters and is used to evaluate system efficiency and behaviors. As an example of Toto's capabilities, we present a study to explore the balance between cluster database density and quality of service.