Advances, Systems and Applications
Table 3 Host overload detection algorithms
From: Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres
Algorithms | Characteristics | |
|---|---|---|
| Â | Depend on the mean of the last CPU used. Unsuitable for dynamic and unpredictable workload | |
Adaptive utilization threshold [18] | Median Absolute Deviation | Depend on statistical dispersion Similar to the static threshold |
Inter-quartile range [130] | Same as Median but compare third and first quartiles Provide poor prediction of host overloading | |
Local regression algorithms | By fitting simple models to CPU utilization | |
regression robust | Use regression to predict the future CPU utilization | |
Markov overload detection | Add constraint in estimation increase the computation | |