  Performance tuning of Database Management Systems(DBMS) is both complex and
challenging as it involves identifying and altering several key performance
tuning parameters. The quality of tuning and the extent of performance
enhancement achieved greatly depends on the skill and experience of the
Database Administrator (DBA). As neural networks have the ability to adapt to
dynamically changing inputs and also their ability to learn makes them ideal
candidates for employing them for tuning purpose. In this paper, a novel tuning
algorithm based on neural network estimated tuning parameters is presented. The
key performance indicators are proactively monitored and fed as input to the
Neural Network and the trained network 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. .
