Self-tuning of Database Management Systems(DBMS) offers important advantages such as improved performance, reduced Total Cost of Ownership(TCO), eliminating the need for an exert Database Administrator(DBA) and improved business prospects. Several techniques have been proposed by researchers and the database vendors to self-tune the DBMS. However, the research focus was confined to physical tuning techniques and the algorithms used in existing methods for self-tuning of memory need analysis of large statistical data. As result, these approaches are not only computationally expensive but also do not adapt well to highly unpredictable workload types and user-load patterns. Hence, in this paper a fuzzy based self-tuning approach has been proposed wherein, three inputs namely, Buffer-Hit-Ratio, Number of Users and Database size are extracted from the Database management system as sensor inputs that indicate degradation in performance and key tuning parameters called the effectors are altered according to the fuzzy-rules. The fuzzy rules are framed after a detailed study of impact of each tuning parameter on the response-time of user queries. The proposed self-tuning architecture is based on Monitor, Analyze, Plan and Execute(MAPE) feedback control loop framework  and has been tested under various workload types. The results have been validated by comparing the performance of the proposed self-tuning system with the auto-tuning feature of commercial database systems. The results show significant improvement in performance under various workload-types, user-load variations.
Software And Hardware
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL