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Fig. 4 Average makespan varying the task heterogeneity (no failures).
Fig. 5 Task heterogeneity indexes and median wages within occupations, across two skill levels.
Fig. 3 Correlations between task heterogeneity indexes and the Gini Index and the frictional wage dispersion (FWD) index.
For all task heterogeneity indices, a positive relation can be observed with the within-occupational median wage.
Medium -and low-skilled occupations do not show any particular pattern of task heterogeneity when this dimension is related to wage levels.
This suggests that task heterogeneity can be related not only to the within-occupation wage heterogeneity but also to the salary scale.
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We then average the task heterogeneities corresponding to each occupation and obtain an occupation-specific value of tasks heterogeneity.
As before, each point is the average of all levels of tasks heterogeneity and tasks granularity.
Figure 4 evaluates how each scheduler behaves with different levels of tasks heterogeneity.
In grid computing, the makespan depends on several parameters, like the number of resources and tasks, the task granularity (task size), the tasks heterogeneity (the variation of the tasks sizes), and the resources heterogeneity (the variation of the resources speeds).
The performance of MFTF becomes worse when the tasks heterogeneity augments, for the reasons discussed above: the higher the difference among tasks sizes, the worse the fitness and the higher the job execution time.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com