Performance Prediction for Performance-Sensitive Queries Based on Algorithmic Complexity

Performance predictions for database queries allow service providers to determine what resources are needed to ensure their performance. Cost-based or rule-based approaches have been proposed to optimize database query execution plans. However, Virtual Machine (VM)-based database services have little or no sharing of resources or interactions between applications hosted on shared infrastructures. Neither providers nor users have the right combination of visibility/access/expertise to perform proper tuning and provisioning. This paper presents a performance prediction model for query execution time estimates based on the query complexity for various data sizes. The user query execution time is a combination of five basic operator complexities: $O\mathsf{}\mathrm{(}\mathrm{1}\mathrm{)}$, $O\mathsf{}\mathrm{(}\log \mathsf{}\mathrm{(}n\mathrm{)}\mathrm{)}$, $O\mathsf{}\mathrm{(}n\mathrm{)}$, $O\mathsf{}\mathrm{(}n\mathsf{}\log \mathsf{}\mathrm{(}n\mathrm{)}\mathrm{)}$, and $O\mathsf{}\mathrm{(}{n}^{\mathrm{2}}\mathrm{)}$. Moreover, tests indicate that not all queries are equally important for performance prediction. As such, this paper illustrates a performance-sensitive query locating process on three benchmarks: RUBiS, RUBBoS, and TPC-W. A key observation is that performance-sensitive queries are only a small proportion ( $\mathrm{20}\mathrm{\%}$) of the application query set. Evaluation of the performance model on the TPC-W benchmark shows that the query complexity in a real life scenario has an average prediction error rate of less than $\mathrm{10}\mathrm{\%}$ which demonstrates the effectiveness of this predictive model.