Deokyeon Jang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Deokyeon Jang has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Hyunsik Yoon
Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
1 shared publication - Kijung Jung
Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
1 shared publication - Yon Dohn Chung
Korea University, Seoul, Republic of Korea
1 shared publication
Latest Publications
- Open Access
- 19 April 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 12, 60138-60148
QHB+: Accelerated Configuration Optimization for Automated Performance Tuning of Spark SQL Applications
Apache Spark stands out as a well-known solution for big data processing because of its efficiency and rapid processing capabilities. One of its modules, Spark SQL, serves as a prominent big data query engine. However, executing Spark SQL applications with massive data can be time-intensive, and the execution time can vary significantly depending on its configurations. Recent studies try to reduce application execution times by searching optimal configurations for applications. While Bayesian optimization is recognized as a powerful method in recent studies for configuration optimization, it faces challenges such as computational costs and time-consuming computations, especially when dealing with large search spaces Due to these challenges, we propose QHB+, designed to rapidly search optimal configurations. QHB+ utilizes the Successive Halving Algorithm-based optimization methods, performing well in hyperparameter optimization of machine learning models, for configuration optimization of Spark SQL applications. Through empirical evaluations against established benchmarks, we show the efficiency of QHB+, highlighting them as swift alternatives to conventional optimization method for optimizing Spark SQL configurations.
- 19 April 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 12, 60138-60148
Open AccessQHB+: Accelerated Configuration Optimization for Automated Performance Tuning of Spark SQL Applications
Apache Spark stands out as a well-known solution for big data processing because of its efficiency and rapid processing capabilities. One of its modules, Spark SQL, serves as a prominent big data query engine. However, executing Spark SQL applications with massive data can be time-intensive, and the execution time can vary significantly depending on its configurations. Recent studies try to reduce application execution times by searching optimal configurations for applications. While Bayesian optimization is recognized as a powerful method in recent studies for configuration optimization, it faces challenges such as computational costs and time-consuming computations, especially when dealing with large search spaces Due to these challenges, we propose QHB+, designed to rapidly search optimal configurations. QHB+ utilizes the Successive Halving Algorithm-based optimization methods, performing well in hyperparameter optimization of machine learning models, for configuration optimization of Spark SQL applications. Through empirical evaluations against established benchmarks, we show the efficiency of QHB+, highlighting them as swift alternatives to conventional optimization method for optimizing Spark SQL configurations.