Name
Abstract | ||
|---|---|---|
The increasing complexity of database management systems (DBMSs) and their workloads means that manually managing their performance has become a difficult and time-consuming task. Autonomic computing systems have emerged as a promising approach to dealing with this complexity. Current DBMSs have begun to move in the direction of autonomic computing with the introduction of parameters that can be dynamically adjusted. A logical next step is the introduction of self-tuning technology to diagnose performance problems and to select the dynamic parameters that must be adjusted. We introduce a method for automatically diagnosing performance problems in DBMSs and then describe how this method can be incorporated into current DBMSs using the concept of reflection. We demonstrate the feasibility of our approach with a proof-of-concept implementation for DB2 universal database. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/IDEAS.2004.1319818 | IDEAS |
Keywords | Field | DocType |
database management systems,self-adjusting systems,DB2 universal database,DBMS complexity,autonomic computing systems,database management systems,self-tuning technology | Data mining,Database tuning,Autonomic computing,Computer science,Database design,Self-tuning,Database server,Database | Conference |
ISSN | ISBN | Citations |
1098-8068 | 0-7695-2168-1 | 7 |
PageRank | References | Authors |
0.60 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| patrick martin | 1 | 148 | 18.22 |
| Wendy Powley | 2 | 329 | 28.43 |
| Darcy Benoit | 3 | 7 | 0.60 |