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Workload Detection and Continuous Automatic Bayesian Optimization in Database Management Systems

Boström, Jonas LU and Olsson, Viktor LU (2022) In LU-CS-EX EDAM05 20221
Department of Computer Science
Abstract
The goal of this thesis has been to investigate the possibility of multi-workload optimization in Database Management Systems and workload detection. A system was successfully constructed to allow for multi-workload testing and data aggregation. The performance gain when optimizing using this project did not seem to match the optimizing performance obtained during testing within the single-workload framework. To test workload detection, data was collected for the benchmarks TPC-C, CH-benchmark and Wikipedia for two different types of metrics. The first was hardware-based metrics which was tested using the change detection technique CUSUM. It was found that hardware-metrics excelled in separating data for the chosen workloads in... (More)
The goal of this thesis has been to investigate the possibility of multi-workload optimization in Database Management Systems and workload detection. A system was successfully constructed to allow for multi-workload testing and data aggregation. The performance gain when optimizing using this project did not seem to match the optimizing performance obtained during testing within the single-workload framework. To test workload detection, data was collected for the benchmarks TPC-C, CH-benchmark and Wikipedia for two different types of metrics. The first was hardware-based metrics which was tested using the change detection technique CUSUM. It was found that hardware-metrics excelled in separating data for the chosen workloads in non-optimizing circumstances, and in optimizing situations it was found to be too unreliable. The second type consisted of the query types that were executed by the Database Management System. When tested with the DBSCAN clustering method all data points were clustered correctly. (Less)
Please use this url to cite or link to this publication:
author
Boström, Jonas LU and Olsson, Viktor LU
supervisor
organization
alternative title
Arbetsidentifiering och Kontinuerlig Automatisk Bayesisk Optimering i Databashanterare
course
EDAM05 20221
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
LU-CS-EX
report number
2022-50
ISSN
1650-2884
language
English
id
9103098
date added to LUP
2022-11-10 15:57:50
date last changed
2022-11-10 15:57:50
@misc{9103098,
  abstract     = {{The goal of this thesis has been to investigate the possibility of multi-workload optimization in Database Management Systems and workload detection. A system was successfully constructed to allow for multi-workload testing and data aggregation. The performance gain when optimizing using this project did not seem to match the optimizing performance obtained during testing within the single-workload framework. To test workload detection, data was collected for the benchmarks TPC-C, CH-benchmark and Wikipedia for two different types of metrics. The first was hardware-based metrics which was tested using the change detection technique CUSUM. It was found that hardware-metrics excelled in separating data for the chosen workloads in non-optimizing circumstances, and in optimizing situations it was found to be too unreliable. The second type consisted of the query types that were executed by the Database Management System. When tested with the DBSCAN clustering method all data points were clustered correctly.}},
  author       = {{Boström, Jonas and Olsson, Viktor}},
  issn         = {{1650-2884}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{LU-CS-EX}},
  title        = {{Workload Detection and Continuous Automatic Bayesian Optimization in Database Management Systems}},
  year         = {{2022}},
}