Enhanced Performance of Database by Automated Self-Tuned Systems

Performance tuning of Database Management Systems (DBMS) is complex as well as challenging task since it involves identification and alteration of several key performance tuning parameters. The quality of tuning and the extent of performance enhancement achieved greatly depend on the skill and experience of the Database Administrator (DBA). The ability of our automated database design to adapt to dynamically changing inputs makes them ideal candidates for employing them for tuning purpose. In this paper, a novel tuning algorithm based on new script estimated tuning parameters is presented. The key performance indicators are proactively monitored and fed as input to the proposed script and the trained networks estimates the suitable size of the buffer cache, shared pool and redo log buffer size. The tuner alters these tuning parameters using the estimated values using a rate change computing algorithm. The preliminary results show that the proposed method is effective in improving the query response time for a variety of workload types. To summarize, this paper presents a self tuned database system or we can say, automated database system whose main focus is performance optimization.