# Fast Performance Modeling across Different Database Versions Using Partitioned Co-Kriging
## 5 Experimental Evaluation
### Evaluation of Prediction Accuracy
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline
**Sample Size** & **1n** & **2n** & **3n** & **4n** & **5n** & **6n** & **7n** & **8n** & **9n** & **10n** & **11n** & **12n** & **13n** & **14n** & **15n** \\ \hline DeepPerf & 21.829 & 24.653 & 21.446 & 22.757 & 23.050 & 23.234 & 21.508 & 21.313 & 20.849 & 21.033 & 15.444 & 19.558 & 16.970 & 16.493 & 17.168 \\ CART & 23.281 & 22.511 & 16.529 & 19.268 & 18.071 & 13.550 & 13.899 & 12.642 & 12.086 & 13.053 & 12.651 & 11.144 & 9.665 & 12.937 & 10.359 \\ Finetune & 14.532 & 13.854 & 13.320 & 12.640 & 12.649 & 13.376 & 13.610 & 14.906 & 14.467 & 12.527 & 14.550 & 13.703 & 14.050 & 14.037 & 13.087 \\ DDC & 19.255 & 25.563 & 26.350 & 21.325 & 21.912 & 22.096 & 24.017 & 18.917 & 19.936 & 19.174 & 19.285 & 19.935 & 17.632 & 15.661 & 16.233 \\ Model-shift & 15.848 & 16.171 & 13.141 & 12.189 & 12.002 & **10.128** & 11.342 & 10.224 & 10.697 & 11.064 & 11.419 & 10.822 & 10.217 & 11.198 & 11.246 \\ Otertune & 16.846 & 19.041 & 18.605 & 21.737 & 19.475 & 19.258 & 19.293 & 18.322 & 19.081 & 18.945 & 17.393 & 19.100 & 17.991 & 17.379 & 16.504 \\ PCK & **12.399** & **12.177** & **10.222** & **10.734** & **10.747** & 10.922 & **9.618** & **9.268** & **9.645** & **9.273** & **9.303** & **9.230** & **9.183** & **9.20** & **9.197** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MRE comparison among different approaches for MySQL (version 5.5–8.0). \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline
**Sample Size** & **1n** & **2n** & **3n** & **4n** & **5n** & **6n** & **7n** & **8n** & **9n** & **10n** & **11n** & **12n** & **13n** & **14n** & **15n** \\ \hline DeepPerf & 10.405 & 7.598 & 6.304 & 5.608 & 5.873 & 6.060 & 5.947 & 5.482 & 5.727 & 5.552 & 5.341 & 5.135 & 5.355 & 5.187 & 5.294 \\ CART & 6.270 & 5.499 & 5.560 & 5.964 & 5.763 & 5.408 & 5.198 & 5.384 & 6.024 & 5.832 & 5.876 & 5.443 & 5.878 & 5.776 & 5.700 \\ Finetune & 4.790 & 5.721 & 5.227 & 4.779 & 5.331 & 5.007 & 5.451 & 4.931 & 5.150 & 4.664 & 4.638 & 4.780 & 4.796 & 4.667 & 4.666 \\ DDC & 9.688 & 7.430 & 6.805 & 5.377 & 5.847 & 5.292 & 5.562 & 5.711 & 5.229 & 5.417 & 5.549 & 5.442 & 5.415 & 5.248 & 5.084 \\ Model-shift & 4.682 & 4.643 & 4.927 & 5.460 & 5.314 & 5.024 & 5.342 & 5.061 & 5.279 & 5.280 & 5.217 & 5.127 & 5.013 & 5.110 & 4.960 \\ Ottertune & 31.177 & 16.059 & 11.747 & 11.413 & 10.210 & 9.895 & 10.033 & 9.929 & 10.089 & 9.111 & 9.168 & 8.769 & 8.299 & 8.222 & 7.718 \\ PCK & **3.278** & **3.083** & **2.931** & **2.877** & **3.036** & **2.889** & **2.886** & **2.879** & **2.889** & **2.868** & **2.873** & **2.877** & **2.867** & **2.870** & **2.867** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: MRE comparison among different approaches for Cassandra (version 2.1.0–3.11.6). \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline
**Sample Size** & **1n** & **2n** & **3n** & **4n** & **5n** & **6n** & **7n** & **8n** & **9n** & **10n** & **11n** & **12n** & **13n** & **14n** & **15n** \\ \hline DeepPerf & 9.637 & 6.384 & 4.997 & 3.835 & 3.577 & 3.525 & 3.209 & 3.219 & 3.276 & 3.12 & 3.031 & 2.828 & 2.816 & 2.828 & 2.819 \\ CART & 3.19 & 3.023 & 3.232 & 3.128 & 3.039 & 3.065 & 2.839 & 2.932 & 2.990 & 2.905 & 2.961 & 2.886 & 2.868 & 2.831 & 2.807 \\ Finetune & 2.778 & 2.767 & 2.838 & 2.677 & 2.725 & 2.591 & 2.729 & 2.763 & 2.783 & **2.522** & 2.595 & **2.552** & 2.621 & 2.61 & 2.593 \\ DDC & 7.415 & 5.619 & 4.507 & 3.749 & 3.385 & 3.382 & 2.968 & 2.928 & 3.095 & 2.943 & 2.767 & 2.756 & 2.714 & 2.809 & 2.757 \\ Model-shift & 2.718 & 2.603 & 2.645 & 2.991 & 2.858 & 2.932 & 2.75 & 2.676 & 2.902 & 2.831 & 2.799 & 2.674 & 2.798 & 2.866 & 2.745 \\ Ottertune & 11.934 & 9.289 & 9.373 & 7.792 & 6.812 & 6.431 & 5.127 & 4.76 & 4.486 & 4.173 & 3.824 & 3.574 & 3.312 & 3.274 & 3.217 \\ PCK & **2.634** & **2.616** & **2.593** & **2.590** & **2.606** & **2.585** & **2.568** & **2.576** & **2.572** & 2.570 & **2.573** & 2.568 & **2.558** & **2.564** & **2.167** \\ \hline \hline \end{tabular}
\end{table}