Workload-level pruning strategies help streamline data collection and improve model training efficiency by removing unimportant queries, thus shortening workload execution times.
Configuration-level pruning enhances search space efficiency, which is crucial given the high-dimensional configurable parameters of Database Management Systems (DBMSs), ultimately contributing to faster and more effective tuning.
Employing smart pruning strategies can significantly mitigate the time-intensive nature of exploring configuration spaces when optimizing DBMS performance across diverse workloads.
The interplay between data collection and configuration optimization necessitates innovative approaches like workload-level and configuration-level pruning to achieve efficient autotuning of database systems.
#database-management-systems #parameter-tuning #workload-optimization #pruning-strategies #machine-learning
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