Demand-driven resource allocation for cloud databases has become a popular research direction. Recent approaches have evolved from reactive policies to proactive decision making. These approaches leverage not only the current resource demand but also the predicted demand to make more informed resource allocation decisions for each database and thus improve the quality of service and reduce the operational costs. We present an infrastructure that enables proactive resource allocation capabilities for millions of serverless Azure SQL databases. Our solution finds near-optimal middle ground between high availability of resources, low operational costs, and low computational overhead of the proactive policy. We describe the design principles we followed and the architectural decisions we made during this cross-team, multi-year journey. Given the size and scope of our solution, we believe that the relational cloud databases in other companies could benefit from the proactive resource allocation capabilities.

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.

Microsoft Azure SQL Database is among the leading relational database service providers in the cloud. Serverless compute automatically scales resources based on workload demand. When a database becomes idle its resources are reclaimed. When activity returns, resources are resumed. Customers pay only for resources they used. However, scaling is currently merely reactive, not proactive, according to customers' workloads. Therefore, resources may not be immediately available when a customer comes back online after a prolonged idle period. In this work, we focus on reducing this delay in resource availability by predicting the pause/resume patterns and proactively resuming resources for each database. Furthermore, we avoid taking away resources for short idle periods to relieve the back-end from ineffective pause/resume workflows. Results of this study are currently being used worldwide to find the middle ground between quality of service and cost of operation.

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 Azure SQL Database is among the leading relational database service providers in the cloud. Serverless compute automatically scales resources based on workload demand. When a database becomes idle its resources are reclaimed. When activity returns, resources are resumed. Customers pay only for resources they used. However, scaling is currently merely reactive, not proactive, according to customers' workloads. Therefore, resources may not be immediately available when a customer comes back online after a prolonged idle period. In this work, we focus on reducing this delay in resource availability by predicting the pause/resume patterns and proactively resuming resources for each database. Furthermore, we avoid taking away resources for short idle periods to relieve the back-end from ineffective pause/resume workflows. Results of this study are currently being used worldwide to find the middle ground between quality of service and cost of operation.

Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost. We employ a Data Science (DS) driven solution to predict user load and leverage these predictions to optimize resource allocation. To this end, we built the Seagull infrastructure that processes per-server telemetry, validates the data, trains and deploys ML models. The models are used to predict customer load per server (24h into the future), and optimize service operations. Seagull continually re-evaluates accuracy of predictions, fallback to previously known good models and triggers alerts as appropriate. We deployed this infrastructure in production for PostgreSQL and MySQL servers across all Azure regions, and applied it to the problem of scheduling server backups during low-load time. This minimizes interference with user-induced load and improves customer experience.

Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost. We employ a Data Science (DS) driven solution to predict user load and leverage these predictions to optimize resource allocation. To this end, we built the Seagull infrastructure that processes per-server telemetry, validates the data, trains and deploys ML models. The models are used to predict customer load per server (24h into the future), and optimize service operations. Seagull continually re-evaluates accuracy of predictions, fallback to previously known good models and triggers alerts as appropriate. We deployed this infrastructure in production for PostgreSQL and MySQL servers across all Azure regions, and applied it to the problem of scheduling server backups during low-load time. This minimizes interference with user-induced load and improves customer experience.

Over-booking cloud resources is an effective way to increase the cost efficiency of a cluster, and is being studied within Microsoft for the Azure SQL Database service. A key challenge is to strike the right balance between the potentially conflicting goals of optimizing for resource allocation efficiency and positive user experience. Understanding when cloud database customers use their database instances and when they are idle can allow one to successfully balance these two metrics. In our work, we formulate and evaluate production-feasible methods to develop idleness profiles for customer databases. Using one of the largest data center telemetry datasets, namely Azure SQL Database telemetry across multiple data centers, we show that our schemes are effective in predicting future patterns of database usage. Our methods are practical and improve the efficiency of clusters while managing customer expectations.

To lower hosting costs and service prices, database-as-a-service (DBaaS) providers strive to maximize cluster utilization without negatively affecting their users' service experience. Some of the most effective approaches for increasing service efficiency result in the over-booking of the cluster with user databases. For instance, one approach is to reclaim cluster capacity from a database when it is idle, temporarily re-using the capacity for some other purpose, and over-booking the cluster's resources. Such approaches are largely driven by policies that determine when it is prudent to temporarily reclaim capacity from an idle database. In this paper, we examine policies that inherently tune the system's idle sensitivity. Increased sensitivity to idleness leads to aggressive over-booking while the converse leads to conservative reclamation and lower utilization levels. Aggressive over-booking also incurs a "reserve" capacity cost (for when we suddenly "owe" capacity to previously idle databases.) We answer these key questions in this paper: (1) how to find a "good" resource reclamation policy for a given DBaaS cluster of users; and (2) how to forecast the needed near-term reserve capacity. To help us answer these questions, we used production user activity traces from Azure SQL DB and built models of an over-booking mechanism. We show that choosing the right policy can substantially boost the efficiency of the service, facilitating lower service prices via lower amortized infrastructure costs.

As traditional and mission-critical relational database workloads migrate to the cloud in the form of Database-as-a-Service (DaaS), there is an increasing motivation to provide performance goals in Service Level Objectives (SLOs). Providing such performance goals is challenging for DaaS providers as they must balance the performance that they can deliver to tenants and the data center's operating costs. In general, aggressively aggregating tenants on each server reduces the operating costs but degrades performance for the tenants, and vice versa. In this paper, we present a framework that takes as input the tenant workloads, their performance SLOs, and the server hardware that is available to the DaaS provider, and outputs a cost-effective recipe that specifies how much hardware to provision and how to schedule the tenants on each hardware resource. We evaluate our method and show that it produces effective solutions that can reduce the costs for the DaaS provider while meeting performance goals.

Cloud SQL Server is a relational database system designed to scale-out to cloud computing workloads. It uses Microsoft SQL Server as its core. To scale out, it uses a partitioned database on a shared-nothing system architecture. Transactions are constrained to execute on one partition, to avoid the need for two-phase commit. The database is replicated for high availability using a custom primary-copy replication scheme. It currently serves as the storage engine for Microsoft's Exchange Hosted Archive and SQL Azure.

When SQL Azure was released in August 2009, it was the first database service of its kind along multiple axes, compared to other Cloud services: shared nothing architecture and log-based replication; support for full ACID properties; providing consistency and high availability; and by offering near 100% compatibility with on-premise SQL Server delivered a familiar programming model at cloud scale. Today, just over two years later, the service has grown to span six hosting regions across three continents; hosting large numbers of databases (in the order 100s of thousands), increasing more than 5x each year with 10s of thousands of subscribers. It is a very busy service, clocking more than 30 million successful logins over a 24 hour period. In this paper we reflect on the lessons learned, and the challenges we will need to face in future, in order to take the SQL Azure service to the next level of scale, performance, satisfaction for the end user, and profitability for the service provider.