Abstrct
Database management systems (DBMSs) are notoriously difficult to deploy and administer. Self-driving DBMSs seek to remove these im- pediments 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 intelli- gence 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 plan- ning. We then describe the system design principles to facilitate holistic autonomous operations. Such prescripts reduce the com- plexity of the problem, thereby enabling a DBMS to converge to a better and more stable configuration more quickly.
https://db.cs.cmu.edu/papers/2021/p3211-pavlo.pdf
Abstrct Database management systems (DBMSs) are notoriously difficult to deploy and administer. Self-driving DBMSs seek to remove these im- pediments 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 intelli- gence 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 plan- ning. We then describe the system design principles to facilitate holistic autonomous operations. Such prescripts reduce the com- plexity of the problem, thereby enabling a DBMS to converge to a better and more stable configuration more quickly.