query performance, data size, query complexity, performance-sensitive query
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(1), O(log(n)), O(n), O(nlog(n)), and O(n2). 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 (20%) 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 10% which demonstrates the effectiveness of this predictive model.
Tsinghua University Press
Chihung Chi, Ye Zhou, Xiaojun Ye. Performance Prediction for Performance-Sensitive Queries Based on Algorithmic Complexity. Tsinghua Science and Technology 2013, 18(6): 618-628.