ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases
Xinyi Zhang, Hong Wu, Zhuo Chang, Shuowei Jin, Jian Tan, Li Fei-Fei, Tieying Zhang, Bin Cui
Abstract
Modern database management systems (DBMS) contain tens to hundreds of critical performance tuning knobs that determine the system runtime behaviors. To reduce the total cost of ownership, cloud database providers put in drastic effort to automatically optimize the resource utilization by tuning these knobs. There are two challenges. First, the tuning system should always abide by the service level agreement (SLA) while optimizing the resource utilization, which imposes strict constrains on the tuning process. Second, the tuning time should be reasonably acceptable since time-consuming tuning is not practical for production and online troubleshooting.
Topics & Concepts
TroubleshootingComputer scienceCloud computingDatabaseResource (disambiguation)Process (computing)Cloud databasePerformance tuningService-level agreementResource management (computing)Resource allocationDistributed computingOperating systemComputer networkMachine Learning and Data ClassificationData Stream Mining TechniquesMachine Learning and Algorithms