Adaptive neuro-fuzzy technique for performance tuning of database management systems

A recent trend in database performance tuning is towards self tuning for some of the important benefits like efficient use of resources, improved performance and low cost of ownership that the auto-tuning offers. Most modern database management systems (DBMS) have introduced several dynamically tunable parameters that enable the implementation of self tuning systems. An appropriate mix of various tuning parameters results in significant performance enhancement either in terms of response time of the queries or the overall throughput. The choice and extent of tuning of the available tuning parameters must be based on the impact of these parameters on the performance and also on the amount and type of workload the DBMS is subjected to. The tedious task of manual tuning and also non-availability of expert database administrators (DBAs), it is desirable to have a self tuning database system that not only relieves the DBA of the tedious task of manual tuning, but it also eliminates the need for an expert DBA. Thus, it reduces the total cost of ownership of the entire software system. A self tuning system also adapts well to the dynamic workload changes and also user loads during peak hours ensuring acceptable application response times. In this paper, a novel technique that combines learning ability of the artificial neural network and the ability of the fuzzy system to deal with imprecise inputs are employed to estimate the extent of tuning required. Furthermore, the estimated values are moderated based on knowledgebase built using experimental findings. The experimental results show significant performance improvement as compared to built in self tuning feature of the DBMS.