Workload-aware Parameter Selection and Performance Prediction for In-memory Databases

• Maria I. V. Lima Universidade Federal do Ceará (UFC)
• Victor A. E de Farias Universidade Federal do Ceará (UFC)
• Francisco D. B. S. Praciano Universidade Federal do Ceará (UFC)
• Javam C. Machado Universidade Federal do Ceará (UFC)

DOI: https://doi.org/10.5753/sbbd.2018.22228

Resumo

In-memory databases, just as hard drive ones, may offer hundreds of customizable settings, making the task of system tuning overwhelming for a database administrator. Even worse, the number of parameters continues to grow over the years and they can affect performance in a not intuitive manner. Models that capture their behavior can assist automatic tuning mechanisms to obtain optimal performance. In this work, we propose a learning-based approach to select the most meaningful parameters and generate a performance model based on both the workload and the database configurations. Experimental results confirm that our approach can create accurate performance models using only a reduced set of selected parameters.

Palavras-chave: In-memory databases, earning-based approach, database configurations, tuning

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