TY  - JOUR
AU  - Chi, Chihung
AU  - Zhou, Ye
AU  - Ye, Xiaojun
PY  - 2013
TI  - Performance Prediction for Performance-Sensitive Queries Based on Algorithmic Complexity
JO  - Tsinghua Science and Technology
SN  - 1007-0214
SP  - 618
EP  - 628
VL  - 18
IS  - 6
AB  - 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⁢(n⁢log⁡(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.
UR  - https://doi.org/10.1109/TST.2013.6678907
DO  - 10.1109/TST.2013.6678907