Tempo: Robust and Self-Tuning Resource Management in Multi-tenant Parallel Databases

Multi-tenant database systems have a component called the Resource Manager, or RM that is responsible for allocating resources to tenants. RMs today do not provide direct support for performance objectives such as: \"Average job response time of tenant A must be less than two minutes\", or \"No more than 5% of tenant B's jobs can miss the deadline of 1 hour.\" Thus, DBAs have to tinker with the RM's low-level configuration settings to meet such objectives. We propose a framework called Tempo that brings simplicity, self-tuning, and robustness to existing RMs. Tempo provides a simple interface for DBAs to specify performance objectives declaratively, and optimizes the RM configuration settings to meet these objectives. Tempo has a solid theoretical foundation which gives key robustness guarantees. We report experiments done on Tempo using production traces of data-processing workloads from companies such as Facebook and Cloudera. These experiments demonstrate significant improvements in meeting desired performance objectives over RM configuration settings specified by human experts.