Advances, Systems and Applications
Table 8 The range of accuracy values for four prediction models
From: Analysis and prediction of virtual machine boot time on virtualized computing environments
Cluster | Data Range | Accuracy of Each VM Boot Time Prediction Model (%) | |||
|---|---|---|---|---|---|
Rule-based (Nguyen et al.’s) | Random Forest Regression (RF) | Regression Tree (RT) | Linear Regression (LR) | ||
4 Hosts | Min | 29.85% | 68.66% | 50.19% | 49.61% |
Max | 97.50% | 100% | 100% | 99.99% | |
Avg | 48.73% | 94.76% | 94.25% | 87.51% | |
7 Hosts | Min | 22.39% | 79.69% | 76.91% | 15.61% |
Max | 88.08% | 100% | 100% | 100%0 | |
Avg | 37.15% | 96.59% | 96.29% | 87.25% | |