Database management systems (DBMSs) are an important part of modern data-driven applications. However, they are notoriously difficult to deploy and administer. There are existing methods that recommend physical design or knob configurations for DBMSs. But most of them require humans to make final decisions and decide when to apply changes. The goal of a self-driving DBMS is to remove the DBMS administration impediments by managing itself autonomously.

In this talk, I present the design of a new self-driving DBMS (NoisePage) that enables such automatic system management. I first discuss a forecasting framework that uses unsupervised clustering and ensemble ML models to efficiently predict the query arrival rates under varying database workload patterns. I then describe NoisePage’s modeling framework that constructs and maintains ML models to predict the behavior of self-driving DBMS actions: the framework decomposes the DBMS architecture into fine-grained operating units to estimate the system’s behavior under unseen configurations. I then introduce our ongoing work for an action planning framework that makes explainable decisions based on the forecasted workload and the modeled behavior. Lastly, I explain how we integrate all the self-driving components into the system.

Zoom Link: https://cmu.zoom.us/j/562649242 (Password 264771)

Bio:
Lin Ma is a PhD candidate from Carnegie Mellon University Computer Science Department advised by Andy Pavlo. He is interested in database systems and machine learning. His research focus has been on designing the architecture for self-driving databases.