Brian Kroth

Principal RSDE

出版物

表示方法:

- Autotuning Systems: Techniques, Challenges, and Opportunities
  
  Brian Kroth, Sergiy Matusevych, Yiwen Zhu
  
  SIGMOD 2025 | June 2025
  
  Publication DOI
- Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment
  
  Yiwen Zhu, Rathijit Sen, Brian Kroth, Sergiy Matusevych, Andreas Mueller, Tengfei Huang, Rahul Challapalli, Weihan Tang, Xin He, Mo Liu, Estera Kot, Sule Kahraman, Arshdeep Sekhon, Dario Bernal, Aditya Lakra, Shaily Fozdar, Dhruv Relwani, Rui Fang, Long Tian, Karuna Sagar Krishna, Ashit Gosalia, Carlo Curino, Subru Krishnan
  
  Companion of the 2025 International Conference on Management of Data | June 2025
  
  DOI Publication
- 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. | October 2024, 巻17: pp. 4269-4272
  
  Publication
- 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
  
  SIGMOD | June 2024
  
  to appear
  
  Publication
- VASIM: Vertical Autoscaling Simulator Toolkit
  
  Anna Pavlenko, Karla Saur, Yiwen Zhu, Brian Kroth, Joyce Cahoon, Jesús Camacho-Rodríguez
  
  IEEE International Conference on Data Engineering (ICDE 2024) | May 2024
  
  Publication
- Performance Roulette: How Cloud Weather Affects ML-Based System Optimization
  
  Johannes Freischuetz, Konstantinos Kanellis, Brian Kroth, Shivaram Venkataraman
  
  ML for Systems Workshop at NeurIPS 2023 | December 2023
  
  Publication
- Towards Building Autonomous Data Services on Azure
  
  Yiwen Zhu, Yuanyuan Tian, Joyce Cahoon, Subru Krishnan, Ankita Agarwal, Rana Alotaibi, Jesús Camacho-Rodríguez, Bibin A 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 C. 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
  
  ACM SIGMOD | June 2023
  
  Publication Publication
- VIP Hashing — Adapting to Skew in Popularity of Data on the Fly
  
  Aarati Kakaraparthy, Jignesh M. Patel, Brian Kroth, Kwanghyun Park
  
  VLDB 2022 | June 2022
  
  Preprint DOI
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 巻51(2): pp. 30-37
  
  Publication DOI
- LlamaTune: Sample-Efficient DBMS Configuration Tuning
  
  Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas C. Müller, Carlo Curino, Shivaram Venkataraman
  
  VLDB 2022 | March 2022
  
  Preprint DOI
- Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | March 2022
  
  Publication
- Lessons learned from the early performance evaluation of Intel Optane DC persistent memory in DBMS
  
  Yinjun Wu, Kwanghyun Park, Rathijit Sen, Brian Kroth, Jae Young Do
  
  2020 Data Management on New Hardware | June 2020
  
  Publication Publication
- 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
  
  DEEM 2020 | June 2020
  
  Publication
- 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
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  PDF
- Optimizing databases by learning hidden parameters of solid state drives
  
  Aarati Kakaraparthy, Jignesh M. Patel, Kwanghyun Park, Brian Kroth
  
  Proceedings of VLDB | December 2019
  
  Publication DOI PDF Publication

- Autotuning Systems: Techniques, Challenges, and Opportunities
  
  Brian Kroth, Sergiy Matusevych, Yiwen Zhu
  
  SIGMOD 2025 | June 2025
  
  Publication DOI
- Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment
  
  Yiwen Zhu, Rathijit Sen, Brian Kroth, Sergiy Matusevych, Andreas Mueller, Tengfei Huang, Rahul Challapalli, Weihan Tang, Xin He, Mo Liu, Estera Kot, Sule Kahraman, Arshdeep Sekhon, Dario Bernal, Aditya Lakra, Shaily Fozdar, Dhruv Relwani, Rui Fang, Long Tian, Karuna Sagar Krishna, Ashit Gosalia, Carlo Curino, Subru Krishnan
  
  Companion of the 2025 International Conference on Management of Data | June 2025
  
  DOI Publication
- 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. | October 2024, 巻17: pp. 4269-4272
  
  Publication
- Performance Roulette: How Cloud Weather Affects ML-Based System Optimization
  
  Johannes Freischuetz, Konstantinos Kanellis, Brian Kroth, Shivaram Venkataraman
  
  ML for Systems Workshop at NeurIPS 2023 | December 2023
  
  Publication
- Towards Building Autonomous Data Services on Azure
  
  Yiwen Zhu, Yuanyuan Tian, Joyce Cahoon, Subru Krishnan, Ankita Agarwal, Rana Alotaibi, Jesús Camacho-Rodríguez, Bibin A 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 C. 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
  
  ACM SIGMOD | June 2023
  
  Publication Publication
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 巻51(2): pp. 30-37
  
  Publication DOI
- Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | March 2022
  
  Publication
- 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
  
  DEEM 2020 | June 2020
  
  Publication
- 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
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  PDF
- Autotuning Systems: Techniques, Challenges, and Opportunities
  
  Brian Kroth, Sergiy Matusevych, Yiwen Zhu
  
  SIGMOD 2025 | June 2025
  
  Publication DOI
- Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment
  
  Yiwen Zhu, Rathijit Sen, Brian Kroth, Sergiy Matusevych, Andreas Mueller, Tengfei Huang, Rahul Challapalli, Weihan Tang, Xin He, Mo Liu, Estera Kot, Sule Kahraman, Arshdeep Sekhon, Dario Bernal, Aditya Lakra, Shaily Fozdar, Dhruv Relwani, Rui Fang, Long Tian, Karuna Sagar Krishna, Ashit Gosalia, Carlo Curino, Subru Krishnan
  
  Companion of the 2025 International Conference on Management of Data | June 2025
  
  DOI Publication
- 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
  
  SIGMOD | June 2024
  
  to appear
  
  Publication
- VASIM: Vertical Autoscaling Simulator Toolkit
  
  Anna Pavlenko, Karla Saur, Yiwen Zhu, Brian Kroth, Joyce Cahoon, Jesús Camacho-Rodríguez
  
  IEEE International Conference on Data Engineering (ICDE 2024) | May 2024
  
  Publication
- Towards Building Autonomous Data Services on Azure
  
  Yiwen Zhu, Yuanyuan Tian, Joyce Cahoon, Subru Krishnan, Ankita Agarwal, Rana Alotaibi, Jesús Camacho-Rodríguez, Bibin A 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 C. 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
  
  ACM SIGMOD | June 2023
  
  Publication Publication
- VIP Hashing — Adapting to Skew in Popularity of Data on the Fly
  
  Aarati Kakaraparthy, Jignesh M. Patel, Brian Kroth, Kwanghyun Park
  
  VLDB 2022 | June 2022
  
  Preprint DOI
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 巻51(2): pp. 30-37
  
  Publication DOI
- LlamaTune: Sample-Efficient DBMS Configuration Tuning
  
  Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas C. Müller, Carlo Curino, Shivaram Venkataraman
  
  VLDB 2022 | March 2022
  
  Preprint DOI
- Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | March 2022
  
  Publication
- Lessons learned from the early performance evaluation of Intel Optane DC persistent memory in DBMS
  
  Yinjun Wu, Kwanghyun Park, Rathijit Sen, Brian Kroth, Jae Young Do
  
  2020 Data Management on New Hardware | June 2020
  
  Publication Publication
- 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
  
  DEEM 2020 | June 2020
  
  Publication
- 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
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  PDF
- Optimizing databases by learning hidden parameters of solid state drives
  
  Aarati Kakaraparthy, Jignesh M. Patel, Kwanghyun Park, Brian Kroth
  
  Proceedings of VLDB | December 2019
  
  Publication DOI PDF Publication
- Autotuning Systems: Techniques, Challenges, and Opportunities
  
  Brian Kroth, Sergiy Matusevych, Yiwen Zhu
  
  SIGMOD 2025 | June 2025
  
  Publication DOI
- 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. | October 2024, 巻17: pp. 4269-4272
  
  Publication
- Performance Roulette: How Cloud Weather Affects ML-Based System Optimization
  
  Johannes Freischuetz, Konstantinos Kanellis, Brian Kroth, Shivaram Venkataraman
  
  ML for Systems Workshop at NeurIPS 2023 | December 2023
  
  Publication
- Lessons learned from the early performance evaluation of Intel Optane DC persistent memory in DBMS
  
  Yinjun Wu, Kwanghyun Park, Rathijit Sen, Brian Kroth, Jae Young Do
  
  2020 Data Management on New Hardware | June 2020
  
  Publication Publication
- 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. | October 2024, 巻17: pp. 4269-4272
  
  Publication
- VASIM: Vertical Autoscaling Simulator Toolkit
  
  Anna Pavlenko, Karla Saur, Yiwen Zhu, Brian Kroth, Joyce Cahoon, Jesús Camacho-Rodríguez
  
  IEEE International Conference on Data Engineering (ICDE 2024) | May 2024
  
  Publication
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 巻51(2): pp. 30-37
  
  Publication DOI
- 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
  
  DEEM 2020 | June 2020
  
  Publication
- Optimizing databases by learning hidden parameters of solid state drives
  
  Aarati Kakaraparthy, Jignesh M. Patel, Kwanghyun Park, Brian Kroth
  
  Proceedings of VLDB | December 2019
  
  Publication DOI PDF Publication

- Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment
  
  Yiwen Zhu, Rathijit Sen, Brian Kroth, Sergiy Matusevych, Andreas Mueller, Tengfei Huang, Rahul Challapalli, Weihan Tang, Xin He, Mo Liu, Estera Kot, Sule Kahraman, Arshdeep Sekhon, Dario Bernal, Aditya Lakra, Shaily Fozdar, Dhruv Relwani, Rui Fang, Long Tian, Karuna Sagar Krishna, Ashit Gosalia, Carlo Curino, Subru Krishnan
  
  Companion of the 2025 International Conference on Management of Data | June 2025
  
  DOI Publication
- 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. | October 2024, 巻17: pp. 4269-4272
  
  Publication
- Data Science Through the Looking Glass
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | June 2022, 巻51(2): pp. 30-37
  
  Publication DOI
- Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines
  
  Fotis Psallidas, Yiwen Zhu, Bojan Karlaš, Jordan Henkel, Matteo Interlandi, Subru Krishnan, Brian Kroth, Venkatesh Emani, Wentao Wu, Ce Zhang, Markus Weimer, Avrilia Floratou, Carlo Curino, Konstantinos Karanasos
  
  SIGMOD Record | March 2022
  
  Publication
- Autotuning Systems: Techniques, Challenges, and Opportunities
  
  Brian Kroth, Sergiy Matusevych, Yiwen Zhu
  
  SIGMOD 2025 | June 2025
  
  Publication DOI
- 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
  
  SIGMOD | June 2024
  
  to appear
  
  Publication
- VASIM: Vertical Autoscaling Simulator Toolkit
  
  Anna Pavlenko, Karla Saur, Yiwen Zhu, Brian Kroth, Joyce Cahoon, Jesús Camacho-Rodríguez
  
  IEEE International Conference on Data Engineering (ICDE 2024) | May 2024
  
  Publication
- Performance Roulette: How Cloud Weather Affects ML-Based System Optimization
  
  Johannes Freischuetz, Konstantinos Kanellis, Brian Kroth, Shivaram Venkataraman
  
  ML for Systems Workshop at NeurIPS 2023 | December 2023
  
  Publication
- Towards Building Autonomous Data Services on Azure
  
  Yiwen Zhu, Yuanyuan Tian, Joyce Cahoon, Subru Krishnan, Ankita Agarwal, Rana Alotaibi, Jesús Camacho-Rodríguez, Bibin A 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 C. 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
  
  ACM SIGMOD | June 2023
  
  Publication Publication
- VIP Hashing — Adapting to Skew in Popularity of Data on the Fly
  
  Aarati Kakaraparthy, Jignesh M. Patel, Brian Kroth, Kwanghyun Park
  
  VLDB 2022 | June 2022
  
  Preprint DOI
- LlamaTune: Sample-Efficient DBMS Configuration Tuning
  
  Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas C. Müller, Carlo Curino, Shivaram Venkataraman
  
  VLDB 2022 | March 2022
  
  Preprint DOI
- Lessons learned from the early performance evaluation of Intel Optane DC persistent memory in DBMS
  
  Yinjun Wu, Kwanghyun Park, Rathijit Sen, Brian Kroth, Jae Young Do
  
  2020 Data Management on New Hardware | June 2020
  
  Publication Publication
- 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
  
  DEEM 2020 | June 2020
  
  Publication
- 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
  
  Conference on Innovative Data Systems Research (CIDR) | January 2020
  
  PDF
- Optimizing databases by learning hidden parameters of solid state drives
  
  Aarati Kakaraparthy, Jignesh M. Patel, Kwanghyun Park, Brian Kroth
  
  Proceedings of VLDB | December 2019
  
  Publication DOI PDF Publication

Brian Kroth

出版物

Autotuning Systems: Techniques, Challenges, and Opportunities

Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment

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

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

VASIM: Vertical Autoscaling Simulator Toolkit

Performance Roulette: How Cloud Weather Affects ML-Based System Optimization

Towards Building Autonomous Data Services on Azure

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

Data Science Through the Looking Glass

LlamaTune: Sample-Efficient DBMS Configuration Tuning

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

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

MLOS: An Infrastructure for Automated Software Performance Engineering

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

Optimizing databases by learning hidden parameters of solid state drives

Autotuning Systems: Techniques, Challenges, and Opportunities

Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment

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

Performance Roulette: How Cloud Weather Affects ML-Based System Optimization

Towards Building Autonomous Data Services on Azure

Data Science Through the Looking Glass

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

MLOS: An Infrastructure for Automated Software Performance Engineering

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

Autotuning Systems: Techniques, Challenges, and Opportunities

Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment

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

VASIM: Vertical Autoscaling Simulator Toolkit

Towards Building Autonomous Data Services on Azure

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

Data Science Through the Looking Glass

LlamaTune: Sample-Efficient DBMS Configuration Tuning

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

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

MLOS: An Infrastructure for Automated Software Performance Engineering

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

Optimizing databases by learning hidden parameters of solid state drives

Autotuning Systems: Techniques, Challenges, and Opportunities

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

Performance Roulette: How Cloud Weather Affects ML-Based System Optimization

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

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

VASIM: Vertical Autoscaling Simulator Toolkit

Data Science Through the Looking Glass

MLOS: An Infrastructure for Automated Software Performance Engineering

Optimizing databases by learning hidden parameters of solid state drives

Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment

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

Data Science Through the Looking Glass

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

Autotuning Systems: Techniques, Challenges, and Opportunities

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

VASIM: Vertical Autoscaling Simulator Toolkit

Performance Roulette: How Cloud Weather Affects ML-Based System Optimization

Towards Building Autonomous Data Services on Azure

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

LlamaTune: Sample-Efficient DBMS Configuration Tuning

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

MLOS: An Infrastructure for Automated Software Performance Engineering

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

Optimizing databases by learning hidden parameters of solid state drives