Name
Abstract | ||
|---|---|---|
AbstractWe demonstrate a self-driving system DBMind, which provides three autonomous capabilities in database, including self-monitoring, self-diagnosis and self-optimization. First, self-monitoring judiciously collects database metrics and detects anomalies (e.g., slow queries and IO contention), which can profile database status while only slightly affecting system performance (<5%). Then, self-diagnosis utilizes an LSTM model to analyze the root causes of the anomalies and automatically detect root causes from a pre-defined failure hierarchy. Next, self-optimization automatically optimizes the database performance using learning-based techniques, including deep reinforcement learning based knob tuning, reinforcement learning based index selection, and encoder-decoder based view selection. We have implemented DBMind in an open source database openGauss and demonstrated real scenarios. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.14778/3476311.3476334 | Hosted Content |
DocType | Volume | Issue |
Journal | 14 | 12 |
ISSN | Citations | PageRank |
2150-8097 | 2 | 0.36 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuanhe Zhou | 1 | 3 | 1.04 |
| Lianyuan Jin | 2 | 3 | 1.73 |
| Ji Sun | 3 | 34 | 2.79 |
| Xinyang Zhao | 4 | 2 | 0.36 |
| Xiang Yu | 5 | 12 | 6.72 |
| Shifu Li | 6 | 3 | 1.73 |
| Tianqing Wang | 7 | 3 | 2.06 |
| Kun Li | 8 | 45 | 13.10 |
| Luyang Liu | 9 | 2 | 2.05 |