research-article

Towards Dynamic and Safe Configuration Tuning for Cloud Databases

Authors:

Bin CuiAuthors Info & Claims

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

Pages 631 - 645

https://doi.org/10.1145/3514221.3526176

Published: 11 June 2022 Publication History

Abstract

Configuration knobs of database systems are essential to achieve high throughput and low latency. Recently, automatic tuning systems using machine learning methods (ML) have shown to find better configurations compared to experienced database administrators (DBAs). However, there are still gaps to apply the existing systems in production environments, especially in the cloud. First, they conduct tuning for a given workload within a limited time window and ignore the dynamicity of workloads and data. Second, they rely on a copied instance and do not consider the availability of the database when sampling configurations, making the tuning expensive, delayed, and unsafe. To fill these gaps, we propose OnlineTune, which tunes the online databases safely in changing cloud environments. To accommodate the dynamicity, OnlineTune embeds the environmental factors as context feature and adopts contextual Bayesian Optimization with context space partition to optimize the database adaptively and scalably. To pursue safety during tuning, we leverage the black-box and the white-box knowledge to evaluate the safety of configurations and propose a safe exploration strategy via subspace adaptation. We conduct evaluations on dynamic workloads from benchmarks and real-world workloads. Compared with the state-of-the-art methods, OnlineTune achieves 14.4% ~165.3% improvement on cumulative performance while reducing 91.0%~99.5% unsafe configuration recommendations.

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Index Terms

Towards Dynamic and Safe Configuration Tuning for Cloud Databases
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Data management systems
    1. Database administration
      1. Autonomous database administration

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cover image ACM Conferences

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

June 2022

2597 pages

ISBN:9781450392495

DOI:10.1145/3514221

General Chair:
Zachary Ives
University of Pennsylvania (USA)
,
Program Chairs:
Angela Bonifati
Lyon 1 University (France)
,
Amr El Abbadi
University of California, Santa Barbara (USA)

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Published: 11 June 2022

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https://dl.acm.org/doi/10.14778/3681954.3682021
Lao JWang YLi YWang JZhang YCheng ZChen WTang MWang J(2024)GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian OptimizationProceedings of the VLDB Endowment10.14778/3659437.365944917:8(1939-1952)Online publication date: 31-May-2024
https://dl.acm.org/doi/10.14778/3659437.3659449
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https://dl.acm.org/doi/10.1145/3671016.3674809
Kanellis KFreischuetz JVenkataraman S(2024)Nautilus: A Benchmarking Platform for DBMS Knob TuningProceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning10.1145/3650203.3663336(72-76)Online publication date: 9-Jun-2024
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