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We use stochastic models to calculate dependability-related metrics for different cloud infrastructures.
This approach uses stochastic models and a multiple-criteria decision-making method to calculate dependability-related metrics, and to rank cloud infrastructures.
The calculated dependability attributes and probability values of the resulting composite service are then used to manually optimize the composite service towards the user's expectations (Gotze et al. [2008]), differing from the solution by Buys ([2011]) and Dillen et al. ([2012]) in which variant services are automatically selected.
We calculated dependability coefficients for test situations in which random raters are used (equation 2) and for a situation using a fixed rater pair (equation 4).
A bottom-up method that use Bayesian reasoning to analyses and calculate entity dependability and integration dependability layer by layer is described.
The technique has also been extended to calculate other dependability metrics, such as availability [21, 24, 25].
The following estimates of the dependability coefficient were calculated running D-studies for varying numbers of judges and occasions (Table 3).
Emphasize dependable behaviors, for "dependability" is a trait that includes timeliness, good quality and good service.
Finally, dependability (phi) coefficients were also calculated for both outcomes in a decision study.
V video, J judges, O occasions Using these variance components, we got a domain-referenced dependability coefficient of 0.633 was calculated by using one judge on one occasion using the formula expressing the composition of the sources of error variance divided by their respective sample size (n).
The dependability model and dependability calculations are presented to document the improved reliability parameters.
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