# Fast Performance Modeling across Different Database Versions Using Partitioned Co-Kriging
## 7 Conclusions and Future Work  
The current approaches target a specific database version where one needs to learn a performance model from scratch for a newer database version. In this paper, we target the use case when the database version updates. We proposed PCK, a fast performance modeling strategy, which is orthogonal to the previously proposed modeling method. By exploiting knowledge pieces from source via both clustering and co-kriging, our proposed PCK approach significantly improves prediction accuracy and reduces excessive measurement effort of performance modeling. Experimental results on five different database systems show that PCK can achieve better performance prediction accuracy with fewer data in target. The MRE reduction of PCK over six state-of-the-art baseline algorithms rages from 30.73% to 60.83% on average. To achieve the same prediction accuracy, PCK can save more than 15 times of measurements in target compared with other approaches. Furthermore, the experimental results verify the effectiveness of transfer learning on performance prediction task.  
Currently, PCK is suitable for the version change scenarios that the correlation of performance responses remains strong. It is possible to introduce a scheme to identify whether the correlation is strong or weak after the database version changes, and this will be one of our future work. Besides, our proposed method focuses on the unchanged parameter in source and target. In the future, we will continue to improve the PCK to take parameter changes (added and forbidden parameters) into consideration when transferring knowledge across different database versions. Note that PCK is proposed for the database version change scenarios. However, there are varying use cases, namely different kinds of environmental changes, such as workload change and hardware change scenarios. We will explore the usability of PCK and its variants in other environmental change scenarios, this is also an interesting future issue.  
Conceptualization, L.B. and R.C.; methodology, L.B. and R.C.; software, S.W. and J.D.; validation, S.W., X.W., Y.D. and R.S.; formal analysis, R.C., L.B. and S.W.; investigation, S.W., X.W., Y.D. and R.S.; resources, L.B.; data curation, S.W., X.W., Y.D. and R.S.; writing--original draft preparation, R.C.; writing--review and editing, L.B. and J.D.; visualization, R.C. and J.D.; supervision, L.B.; project administration, R.C. and X.W.; funding acquisition, L.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Key R&D Program of China under Grant No. 2018 YFC0831200, in part by National Natural Science Foundation of China under Grant No. 61202040 with Xidian University, in part by the Key R&D Program of Shaaxi under Grant No. 2019 ZDLGY13-03-02, in part by Natural Science Foundation of Shaaxi Province, China under Grant No. 2019 JM-368, and in part by the Key R&D Program of Hebei under Grant No. 20310102D.  
Not applicable.  
Data Availability Statement: Data available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/xdbdilab/PCK](https://github.com/xdbdilab/PCK)]([https://github.com/xdbdilab/PCK](https://github.com/xdbdilab/PCK)).