Toto Benchmarking the Efficiency of a Cloud Service

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.