Towards instance-optimized data systems
Name
p3222-kraska.pdf
Description
Published version
Size
687.01 KB
Format
Adobe PDF
Checksum (MD5)
65583f75ee4f11166174be25bb9f7399
Author(s)
Kraska, Tim
Date Issued
2021
Journal
Proceedings of the VLDB Endowment
Publisher
VLDB Endowment
Citation
Kraska, Tim. 2021. "Towards instance-optimized data systems." Proceedings of the VLDB Endowment, 14 (12).
Version
Final published version
Abstract
In recent years, we have seen increased interest in applying machine learning to system problems. For example, there has been work on applying machine learning to improve query optimization, indexing, storage layouts, scheduling, log-structured merge trees, sorting, compression, and sketches, among many other data management tasks. Arguably, the ideas behind these techniques are similar: machine learning is used to model the data and/or workload in order to derive a more efficient algorithm or data structure. Ultimately, these techniques will allow us to build "instance-optimized" systems: that is, systems that self-adjust to a given workload and data distribution to provide unprecedented performance without the need for tuning by an administrator. While many of these techniques promise orders-of-magnitude better performance in lab settings, there is still general skepticism about how practical the current techniques really are.
The following is intended as a progress report on ML for Systems and its readiness for real-world deployments, with a focus on our projects done as part of the Data Systems and AI Lab (DSAIL) at MIT By no means is it a comprehensive overview of all existing work, which has been steadily growing over the past several years not only in the database community but also in the systems, networking, theory, PL, and many other adjacent communities.
The following is intended as a progress report on ML for Systems and its readiness for real-world deployments, with a focus on our projects done as part of the Data Systems and AI Lab (DSAIL) at MIT By no means is it a comprehensive overview of all existing work, which has been steadily growing over the past several years not only in the database community but also in the systems, networking, theory, PL, and many other adjacent communities.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.14778/3476311.3476392
