Brian Kroth

According to our database, Brian Kroth authored at least 15 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

MLOS in Action: Bridging the Gap Between Experimentation and Auto-Tuning in the Cloud.

Brian Kroth, Sergiy Matusevych, Rana Alotaibi, Yiwen Zhu, Anja Gruenheid, Yuanyuan Tian

Proc. VLDB Endow., August, 2024

Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All?

Rana Alotaibi, Yuanyuan Tian, Stefan Grafberger, Jesús Camacho-Rodríguez, Nicolas Bruno, Brian Kroth, Sergiy Matusevych, Ashvin Agrawal, Mahesh Behera, Ashit Gosalia, César A. Galindo-Legaria, Milind Joshi, Milan Potocnik, Beysim Sezgin, Xiaoyu Li, Carlo Curino

CoRR, 2024

Vertically Autoscaling Monolithic Applications with CaaSPER: Scalable Container-as-a-Service Performance Enhanced Resizing Algorithm for the Cloud.

Anna Pavlenko, Joyce Cahoon, Yiwen Zhu, Brian Kroth, Michael Nelson, Andrew Carter, David Liao, Travis Wright, Jesús Camacho-Rodríguez, Karla Saur

Proceedings of the Companion of the 2024 International Conference on Management of Data, 2024

VASIM: Vertical Autoscaling Simulator Toolkit.

Anna Pavlenko, Karla Saur, Yiwen Zhu, Brian Kroth, Joyce Cahoon, Jesús Camacho-Rodríguez

Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

2023

Towards Building Autonomous Data Services on Azure.

Yiwen Zhu, Yuanyuan Tian, Joyce Cahoon, Subru Krishnan, Ankita Agarwal, Rana Alotaibi, Jesús Camacho-Rodríguez, Bibin Chundatt, Andrew Chung, Niharika Dutta, Andrew Fogarty, Anja Gruenheid, Brandon Haynes, Matteo Interlandi, Minu Iyer, Nick Jurgens, Sumeet Khushalani, Brian Kroth, Manoj Kumar, Jyoti Leeka, Sergiy Matusevych, Minni Mittal, Andreas Müller, Kartheek Muthyala, Harsha Nagulapalli, Yoonjae Park, Hiren Patel, Anna Pavlenko, Olga Poppe, Santhosh Ravindran, Karla Saur, Rathijit Sen, Steve Suh, Arijit Tarafdar, Kunal Waghray, Demin Wang, Carlo Curino, Raghu Ramakrishnan

Proceedings of the Companion of the 2023 International Conference on Management of Data, 2023

2022

Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines.

Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, K. Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos

SIGMOD Rec., 2022

LlamaTune: Sample-Efficient DBMS Configuration Tuning.

Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, Shivaram Venkataraman

Proc. VLDB Endow., 2022

VIP Hashing - Adapting to Skew in Popularity of Data on the Fly.

Aarati Kakaraparthy, Jignesh M. Patel, Brian Kroth, Kwanghyun Park

Proc. VLDB Endow., 2022

VIP Hashing - Adapting to Skew in Popularity of Data on the Fly (extended version).

Aarati Kakaraparthy, Jignesh M. Patel, Brian P. Kroth, Kwanghyun Park

CoRR, 2022

2020

Learning How To Learn Within An LSM-based Key-Value Store.

Yifan Dai, Yien Xu, Aishwarya Ganesan, Ramnatthan Alagappan, Brian Kroth, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau

CoRR, 2020

MLOS: An Infrastructure for Automated Software Performance Engineering.

Carlo Curino, Neha Godwal, Brian Kroth, Sergiy Kuryata, Greg Lapinski, Siqi Liu, Slava Oks, Olga Poppe, Adam Smiechowski, Ed Thayer, Markus Weimer, Yiwen Zhu

Proceedings of the Fourth Workshop on Data Management for End-To-End Machine Learning, 2020

From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees.

Yifan Dai, Yien Xu, Aishwarya Ganesan, Ramnatthan Alagappan, Brian Kroth, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau

Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation, 2020

Lessons learned from the early performance evaluation of Intel optane DC persistent memory in DBMS.

Yinjun Wu, Kwanghyun Park, Rathijit Sen, Brian Kroth, Jaeyoung Do

Proceedings of the 16th International Workshop on Data Management on New Hardware, 2020

Cloudy with high chance of DBMS: a 10-year prediction for Enterprise-Grade ML.

Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Godwal, Matteo Interlandi, Alekh Jindal, Konstantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen Zhu

Proceedings of the 10th Conference on Innovative Data Systems Research, 2020

2019

Optimizing Databases by Learning Hidden Parameters of Solid State Drives.

Aarati Kakaraparthy, Jignesh M. Patel, Kwanghyun Park, Brian Kroth

Proc. VLDB Endow., 2019