Graduation Year
2010
Document Type
Thesis
Degree
M.S.C.S.
Degree Granting Department
Computer Science
Major Professor
Yicheng Tu, Ph.D.
Committee Member
Miguel Labrador, Ph.D.
Committee Member
Gang Ding, Ph.D.
Keywords
Workload Management, Memory Contention, Control Theory, Experimental Platform, Optimal Multiple Query Processing
Abstract
Databases are very complex systems that require database system administrators to perform system tuning in order to achieve optimal performance. Memory tuning is vital to the performance of a database system because when the database workload exceeds its memory capacity, the results of the queries running on a system are delayed and can cause substantial user dissatisfaction. In order to solve this problem, this thesis presents a platform modeled after a closed control feedback loop to control the level of multi-query processing. Utilizing this platform provides two key assets. First, the system identification is acquired, which is one of two crucial steps involved in developing a closed feedback loop. Second, the platform provides a means to experimentally study database tuning problem and verify the effectiveness of research ideas related to database performance.
Scholar Commons Citation
Burrell, Tiffany, "System Identification in Automatic Database Memory Tuning" (2010). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/1583
