Predictability is an important virtue that has long been neglected by the database research community. However, with the performance of modern database systems becoming more and more unpredictable, the task of provisioning and tuning databases has become more and more difficult.
In this project, we aim to bring back such an important aspect of database systems by redesigning certain components of the database systems, so that the database systems not only provides high performance, but also maintains such high level of performance.
Contention-aware lock scheduling for transactional databases.
Boyu Tian, Jiamin Huang, Barzan Mozafari, and Grant Schoenebeck
In Proceedings of 44th International Conference on Very Large Databases (PVLDB), Rio de Janeiro, Brazil, August 27-31, 2018
(Adopted by MySQL 8.0.3+) Technical Report
Statistical Analysis of Latency Through Semantic Profiling.
Jiamin Huang, Barzan Mozafari and Thomas F. Wenisch
In Proceedings of Proceedings of the European Conference on Computer Systems (EuroSys), Belgrade, Serbia, April 23-26, 2017
A Top-Down Approach to Achieving Performance Predictability in Database Systems.
Jiamin Huang, Barzan Mozafari, Grant Schoenebeck, Thomas F. Wenisch
In Proceedings of Proceedings of the ACM SIGMOD 2017 Conference, Chicago, IL, United States, May 14-19, 2017
Identifying the Major Sources of Variance in Transaction Latencies: Towards More Predictable Databases.
Jiamin Huang, Barzan Mozafari, Grant Schoenebeck, and Thomas Wenisch
In Technical Report, March, 2016
DBSeer: Pain-free Database Administration through Workload Intelligence.
Dong Young Yoon, Barzan Mozafari, and Douglas P. Brown
In Proceedings of the 41st International Conference on Very Large Data Bases (PVLDB), Kohala Coast, Hawai'i, U.S.A., September 01-04, 2015
CliffGuard: A Principled Framework for Finding Robust Database Designs.
Barzan Mozafari, Eugene Zhen Ye Goh, and Dong Young Yoon
In Proceedings of the ACM SIGMOD 2015 Conference, Melbourne, VIC, Australia, May 31 - June 04, 2015
Program Manager: Maria Zemankova
This project has started on February 1, 2016 and will end on January 31, 2021 (Estimated).
This material is based upon the work supported by the National Science Foundation under Grant No. 1553169
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.