• Journal of Information Technology Applications and Management
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• v.15 no.1
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• pp.271-282
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• 2008

Database administrators are demanded to acquire much knowledges and take great efforts for keeping consistent performance in system. Various principles, methods, and tools have been proposed in many studies and commercial products in order to alleviate such burdens on database administrators, and it has resulted to the automation of DBMS which reduces the intervention of database administrator. This paper suggests a resource selection method that estimates the status of the database system based on the workload characteristics and that recommends tuneable resources. Our method tries to simplify selection information on DBMS status using data-mining techniques, enhance the accuracy of the selection model, and recommend tuneable resource. For evaluating the performance of our method, instances are collected in TPC-C and TPC-W workloads, and accuracy are calculated using 10 cross validation method, comparisons are made between our scheme and the method which uses only the classification procedure without any simplification of informations. It is shown that our method has over 90% accuracy and can perform tuneable resource selection.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.11a
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• pp.67-67
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• 2007

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.183-190
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• 2006

Database workloads may show different resource usages for database applications. Database administrators can enhance the DBMS performances through resource management that reflects workload characteristics. We provide a method that can identify tunable resources from analyzing the relationship between performance indicators and resources. First, we select which performance indicators increase or decrease by expanding resources using a correlation coefficient and a significance level test. Next, we identify resources that can affect the DBMS Performances by using increasing or decreasing performance indicators. We evaluated our method in the TPC-C and TPC-W environments.