The Psychic-Skeptic Prediction framework for effective monitoring of DBMS workloads
Data & Knowledge Engineering
Surveying the landscape: an in-depth analysis of spatial database workloads
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Self-protecting and self-optimizing database systems: implementation and experimental evaluation
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
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Database administrators should be aware of resource usages to maintain database system performance. As database applications become more complex and diverse, managing database systems becomes too costly and prone to error. Autonomic database tuning becomes more important than ever. This paper starts with an analysis on how resource usages respond by changing resource sizes in database systems. Then, we present a simple method that automatically selects resources that affect the system performance. An experiment using the TPC-C and TPC-W workloads has been carried out with a commercial database system. A preliminary analysis shows that our method works.