Towards a General Framework for ML-based Self-tuning Databases
| Autor: | Nikolas Ioannou, Thomas Schmied, Andreas Döring, Diego Didona, Thomas Parnell |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Database Computer science Bayesian optimization Self-tuning Databases (cs.DB) 020206 networking & telecommunications Context (language use) 02 engineering and technology computer.software_genre Machine Learning (cs.LG) Domain (software engineering) Random search Computer Science - Databases 020204 information systems 0202 electrical engineering electronic engineering information engineering Reinforcement learning Baseline (configuration management) computer Throughput (business) |
| Zdroj: | EuroMLSys@EuroSys |
| Popis: | Machine learning (ML) methods have recently emerged as an effective way to perform automated parameter tuning of databases. State-of-the-art approaches include Bayesian optimization (BO) and reinforcement learning (RL). In this work, we describe our experience when applying these methods to a database not yet studied in this context: FoundationDB. Firstly, we describe the challenges we faced, such as unknown valid ranges of configuration parameters and combinations of parameter values that result in invalid runs, and how we mitigated them. While these issues are typically overlooked, we argue that they are a crucial barrier to the adoption of ML self-tuning techniques in databases, and thus deserve more attention from the research community. Secondly, we present experimental results obtained when tuning FoundationDB using ML methods. Unlike prior work in this domain, we also compare with the simplest of baselines: random search. Our results show that, while BO and RL methods can improve the throughput of FoundationDB by up to 38%, random search is a highly competitive baseline, finding a configuration that is only 4% worse than the, vastly more complex, ML methods. We conclude that future work in this area may want to focus more on randomized, model-free optimization algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|