{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1391"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1391","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Automatic knobs-tuning for DB2 using deep reinforcement learning","abstract":"Modern database management systems have hundreds of different configuration parameters (knobs) that control various aspects of how they behave and perform. These knobs must be properly tuned in order to maximize the performance of the database for a given query workload. Traditionally, database administrators would be responsible for database performance tuning. However, manual configuration tuning is a difficult process for humans, as there are hundreds of different inter-dependent knobs to be tuned. Different queries and workloads also benefit from configurations differently, there is no one single database configuration that can fit all scenarios. We propose BLUTune, a system to automatically produce effective knob configuration for IBM DB2. BLUTune utilizes deep reinforcement learning and features a unique transfer-learning approach to training which allows for fast learning. In experimental validation, BLUTune demonstrates its capability of producing effective configurations across differing sizes of the TPC-DS OLAP benchmark in a timely manner.","abstract_html":"Modern database management systems have hundreds of different configuration parameters (knobs) that control various aspects of how they behave and perform. These knobs must be properly tuned in order to maximize the performance of the database for a given query workload. Traditionally, database administrators would be responsible for database performance tuning. However, manual configuration tuning is a difficult process for humans, as there are hundreds of different inter-dependent knobs to be tuned. Different queries and workloads also benefit from configurations differently, there is no one single database configuration that can fit all scenarios. We propose BLUTune, a system to automatically produce effective knob configuration for IBM DB2. BLUTune utilizes deep reinforcement learning and features a unique transfer-learning approach to training which allows for fast learning. In experimental validation, BLUTune demonstrates its capability of producing effective configurations across differing sizes of the TPC-DS OLAP benchmark in a timely manner.","abstract_has_math":false,"creators":["Bryson, Spencer C."],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Szlichta, Jarek"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-12-01","date_published":"2021-12-01","updated_at":"2026-07-24T05:35:32Z","subjects":["Database tuning","Knob tuning","Deep reinforcement learning","DB2"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1391","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Szlichta, Jarek"]},{"key":"dc:creator","label":"Author","values":["Bryson, Spencer C."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-01-17T19:31:51Z","2022-03-29T17:27:21Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-01-17T19:31:51Z","2022-03-29T17:27:21Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-12-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Database tuning","Knob tuning","Deep reinforcement learning","DB2"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1391"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Modern database management systems have hundreds of different configuration parameters (knobs) that control various aspects of how they behave and perform. These knobs must be properly tuned in order to maximize the performance of the database for a given query workload. Traditionally, database administrators would be responsible for database performance tuning. However, manual configuration tuning is a difficult process for humans, as there are hundreds of different inter-dependent knobs to be tuned. Different queries and workloads also benefit from configurations differently, there is no one single database configuration that can fit all scenarios. We propose BLUTune, a system to automatically produce effective knob configuration for IBM DB2. BLUTune utilizes deep reinforcement learning and features a unique transfer-learning approach to training which allows for fast learning. In experimental validation, BLUTune demonstrates its capability of producing effective configurations across differing sizes of the TPC-DS OLAP benchmark in a timely manner."]},{"key":"dc:title","label":"Title","values":["Automatic knobs-tuning for DB2 using deep reinforcement learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Szlichta, Jarek"],"dc:creator":["Bryson, Spencer C."],"dc:date.accessioned":["2022-01-17T19:31:51Z","2022-03-29T17:27:21Z"],"dc:date.available":["2022-01-17T19:31:51Z","2022-03-29T17:27:21Z"],"dc:date.issued":["2021-12-01"],"dc:description.abstract":["Modern database management systems have hundreds of different configuration parameters (knobs) that control various aspects of how they behave and perform. These knobs must be properly tuned in order to maximize the performance of the database for a given query workload. Traditionally, database administrators would be responsible for database performance tuning. However, manual configuration tuning is a difficult process for humans, as there are hundreds of different inter-dependent knobs to be tuned. Different queries and workloads also benefit from configurations differently, there is no one single database configuration that can fit all scenarios. We propose BLUTune, a system to automatically produce effective knob configuration for IBM DB2. BLUTune utilizes deep reinforcement learning and features a unique transfer-learning approach to training which allows for fast learning. In experimental validation, BLUTune demonstrates its capability of producing effective configurations across differing sizes of the TPC-DS OLAP benchmark in a timely manner."],"dc:identifier.uri":["https://hdl.handle.net/10155/1391"],"dc:language.iso":["en"],"dc:subject":["Database tuning","Knob tuning","Deep reinforcement learning","DB2"],"dc:title":["Automatic knobs-tuning for DB2 using deep reinforcement learning"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:32Z"}