ADWTune: an adaptive dynamic workload tuning system with deep reinforcement learning
Abstract In order to reduce the burden of DBA, the knob tuning method based on reinforcement learning has been proposed and achieved good results in some cases. However, the performance of these solutions is not ideal as the workload features are not considered enough. To address these issues, we pr...
Saved in:
| Main Authors: | Cuixia Li, Junhai Wang, Jiahao Shi, Liqiang Liu, Shuyan Zhang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-02-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01801-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
by: Shuhan Chen, et al.
Published: (2025-06-01) -
Meta reinforcement learning based dynamic tuning for blockchain systems in diverse network environments
by: Yue Pei, et al.
Published: (2025-06-01) -
Door Ring, Knocker and Mirrors in Bursa/Cumalikizik Houses
by: Zerrin Köşklü, et al.
Published: (2024-01-01) -
Enhancing Security of Databases through Anomaly Detection in Structured Workloads
by: Charanjeet Dadiyala, et al.
Published: (2025-02-01) -
Enhancing Security of Databases through Anomaly Detection in Structured Workloads
by: Charanjeet Dadiyala, et al.
Published: (2025-02-01)