Tuning Database Configuration Parameters with iTuned

By Songyun Duan, Vamsidhar Thummala, Shivnath Babu. Proceedings of the VLDB Endowment, 2009

344 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/54a08a0e23fa3c7d9a392f3e339136bade094ed4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Steven Euijong Whang
2. Andrew Pavlo
3. Chuanxiong Guo
4. Carlo Curino
5. S. Zdonik
6. Geoffrey J. Gordon
7. Yibo Zhu
8. Guoliang Li

In Management Science and Operations Research

1. Steven Euijong Whang
2. Carlo Curino
3. Chuan Wu
4. Neoklis Polyzotis
5. Andrew Pavlo
6. S. Zdonik
7. Geoffrey J. Gordon
8. Guoliang Li

In Signal Processing

1. Steven Euijong Whang
2. Carlo Curino
3. Andrew Pavlo
4. S. Zdonik
5. Geoffrey J. Gordon
6. Guoliang Li
7. Yuji Roh

Citations in the corpus

• A generic communication scheduler for distributed DNN training acceleration (2019, methodology): “BO is also used in searching the best cloud configuration for big data analytics [8], tuning the learning parameters of machine learning algorithm [34], and online parameter tuning of databases [16].”
• Automatic Database Management System Tuning Through Large-scale Machine Learning (2017, methodology): “number of experiments that provide an approximate sampling of the entire search space [24].”
• Data-centric Artificial Intelligence: A Survey (2023, unknown): “To optimize these metrics, various parameter-tuning techniques have been proposed, including controlling database configuration settings (e.g., buffer pool size) and runtime operations (e.g., percentage of CPU usage and multi-programming level) [64].”
• Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems (2012, methodology): “Using an analytical cost model is an established technique in automatic database design and optimization [13, 19], as it allows one to determine whether one design choice is better than others and can guide the search process towards a solution that accentuates the properties that are important in a”
• An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning (2019, methodology): “There are two classes of representative studies in DBMS configuration tuning: search-based methods [55] and learning-based methods [4, 14, 35].”
• Data Management Challenges in Production Machine Learning (2017, methodology): “The latter is of course challenging due to the overhead of running these experiments, but we may be able to leverage previous works on DB-parameter optimization [6] in order to schedule these experiments effectively.”