Workload management for cloud databases via machine learning

As elastic IaaS clouds continue to become more cost efficient than on-site datacenters, a wide range of data management applications are migrating to pay-as-you-go cloud computing resources. These diverse applications come with an equally diverse set of performance goals, resource demands, and budget constraints. While existing research has tackled individual tasks such as query placement, scheduling, and resource provisioning to meet these goals and constraints, these techniques fail to provide end-to-end customizable workload management solutions, leading application developers to hand-craft custom heuristics that fit their workload specifications and performance goals. In this vision paper, we argue that workload management challenges can be addressed by leveraging machine learning algorithms. These algorithms can be trained on application-specific properties and performance metrics to automatically learn how to provision resources as well as distribute and schedule the execution of incoming query workloads. Towards this goal, we sketch our vision of WiSeDB, a learning-based service that relies on supervised and reinforcement learning to generate workload management strategies for both static and dynamic workloads.