Lightweight Data Mining Based Database System Self-optimization: A Case Study

Authors:
Xiongpai Qin;Wei Cao;Shan Wang
Affiliations:
Key Laboratory of Data Engineering and Knowledge Engineering, School of Information, Renmin University of China, MOE, Beijing, P.R. China 100872;Key Laboratory of Data Engineering and Knowledge Engineering, School of Information, Renmin University of China, MOE, Beijing, P.R. China 100872;Key Laboratory of Data Engineering and Knowledge Engineering, School of Information, Renmin University of China, MOE, Beijing, P.R. China 100872
Venue:
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Year:
2009

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Abstract

With the system becoming more complex and workloads becoming more fluctuating, it is very hard for DBA to quickly analyze performance data and optimize the system, self optimization is a promising technique. A data mining based optimization scheme for the lock table in database systems is presented. After trained with performance data, a neural network become intelligent enough to predict system performance with newly provided configuration parameters and performance data. During system running, performance data is collected continuously for a rule engine, which chooses the proper parameter of the lock table for adjusting, the rule engine relies on the trained neural network to precisely provide the amount of adjustment. The selected parameter is adjusted accordingly. The scheme is implemented and tested with TPC-C workload, system throughput increases by about 16 percent.