Database systems typically have many knobs that must be configured by database administrators to achieve high performance. RocksDB achieves fast data writing performance using a log-structured merge-tree. This database contains many knobs related to write and space amplification, which are important performance indicators in RocksDB. Previously, it was proved that significant performance improvements could be achieved by tuning database knobs. However, tuning multiple knobs simultaneously is a laborious task owing to the large number of potential configuration combinations and trade-offs. To address this problem, we built a tuning system for RocksDB. First, we generated a valuable RocksDB data repository for analysis and tuning. To find the workload that is most similar to a target workload, we created a new representation for workloads. We then applied the Mahalanobis distance to create a combined workload that is as close to the original target workload as possible. Subsequently, we trained a deep neural network model with the combined workload and used it as the fitness function of a genetic algorithm. Finally, we applied the genetic algorithm to find the best solution for the original target workload. The experimental results demonstrated that the proposed system achieved a significant performance improvement for various target workloads.
|Title of host publication||GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||9|
|Publication status||Published - 2022 Jul 8|
|Event||2022 Genetic and Evolutionary Computation Conference, GECCO 2022 - Virtual, Online, United States|
Duration: 2022 Jul 9 → 2022 Jul 13
|Name||GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference|
|Conference||2022 Genetic and Evolutionary Computation Conference, GECCO 2022|
|Period||22/7/9 → 22/7/13|
Bibliographical noteFunding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (IITP-2017-0-00477, (SW starlab) Research and development of the high performance in-memory distributed DBMS based on flash memory storage in IoT environment)
© 2022 ACM.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Theoretical Computer Science