Virtualizing Cloud Data Infrastructures with BRAD
Name
3722212.3725141.pdf
Size
1.28 MB
Format
Adobe PDF
Checksum (MD5)
6cf257194be76770f0d5069d83bccb19
Author(s)
Yu, Geoffrey
Wu, Ziniu
Kossmann, Ferdi
Li, Tianyu
Markakis, Markos
Ngom, Amadou
Zhang, Sophie
Kraska, Tim
Madden, Samuel
Date Issued
June 22, 2025
Publisher
ACM|Companion of the 2025 International Conference on Management of Data
Citation
Geoffrey X. Yu, Ziniu Wu, Ferdi Kossmann, Tianyu Li, Markos Markakis, Amadou Ngom, Sophie Zhang, Tim Kraska, and Samuel Madden. 2025. Virtualizing Cloud Data Infrastructures with BRAD. In Companion of the 2025 International Conference on Management of Data (SIGMOD/PODS '25). Association for Computing Machinery, New York, NY, USA, 271–274.
Version
Final published version
Abstract
Organizations usually manage their data using multiple specialized cloud database engines (e.g., Aurora, BigQuery, etc.). However, designing and managing multi-engine infrastructures is hard; there can be many designs, each with different performance and costs. Changing the design afterwards (e.g., due to growth) is even more challenging since application code usually ends up tightly coupled to the engines. We propose data infrastructure virtualization. The key idea is to declare a set of virtual database engines (VDBEs), which specify an engine's application-facing properties (e.g., query interface, performance) and its tables, but do not prescribe a concrete engine. An automated planner then decides how to best realize the VDBEs onto physical engines based on the workload. Clients connect to VDBE endpoints and are oblivious to the underlying physical engines-allowing for seamless infrastructure changes. We implemented VDBEs and an automated planner in BRAD: the first data infrastructure virtualization runtime. Our demo will showcase VDBEs and BRAD's automated planner under different workloads.
Description
SIGMOD-Companion ’25, Berlin, Germany
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3722212.3725141
