A generic auto-provisioning framework for cloud databases

TL;DR: An extensible framework is described that, given a target query workload, continually optimizes the system's operational cost, estimated based on the IaaS provider's pricing model, while satisfying QoS expectations.

Abstract: We discuss the problem of resource provisioning for database management systems operating on top of an Infrastructure-As-A-Service (IaaS) cloud. To solve this problem, we describe an extensible framework that, given a target query workload, continually optimizes the system's operational cost, estimated based on the IaaS provider's pricing model, while satisfying QoS expectations. Specifically, we describe two different approaches, a “white-box” approach that uses a fine-grained estimation of the expected resource consumption for a workload, and a “black-box” approach that relies on coarse-grained profiling to characterize the workload's end-to-end performance across various cloud resources. We formalize both approaches as a constraint programming problem and use a generic constraint solver to efficiently tackle them. We present preliminary experimental numbers, obtained by running TPC-H queries with PostsgreSQL on Amazon's EC2, that provide evidence of the feasibility and utility of our approaches. We also briefly discuss the pertinent challenges and directions of on-going research.