Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation
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DOI：
10.14778/3476311.3476411
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发表时间：
2021-07
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影响因子：
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通讯作者：
Andrew Pavlo;Matthew Butrovich;Lin Ma;Prashanth Menon;Wan Shen Lim;Dana Van Aken;William Zhang
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作者：
Andrew Pavlo;Matthew Butrovich;Lin Ma;Prashanth Menon;Wan Shen Lim;Dana Van Aken;William Zhang
众所周知，数据库管理系统（DBMS）很难部署和管理DBMS。但是，鉴于这一目标，我们的最新进步已经使这一目标更加紧密。自动驾驶DBM。 （2）行为建模和（3）行动计划。从而使DBMS能够更快地收敛到更好，更稳定的配置。
Database management systems (DBMSs) are notoriously difficult to deploy and administer. Self-driving DBMSs seek to remove these impediments by managing themselves automatically. Despite decades of DBMS auto-tuning research, a truly autonomous, self-driving DBMS is yet to come. But recent advancements in artificial intelligence and machine learning (ML) have moved this goal closer. Given this, we present a system implementation treatise towards achieving a self-driving DBMS. We first provide an overview of the NoisePage self-driving DBMS that uses ML to predict the DBMS’s behavior and optimize itself without human support or guidance. The system’s architecture has three main ML-based components: (1) workload forecasting, (2) behavior modeling, and (3) action planning. We then describe the system design principles to facilitate holistic autonomous operations. Such prescripts reduce the complexity of the problem, thereby enabling a DBMS to converge to a better and more stable configuration more quickly.